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Latest snapshot2026-09-11USmethodology registry-public-v1
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113,018 tools
- Show account overviewwhoami · Show the signed-in customer and what they can do; call first after connecting, or when asked about login, account, or available features.
Returns:
user email/name, project counts with recent projects and their license
state, capabilities with suggested prompts, and resource links.FluxBuilderFluxBuilder
PluginrequiredDeveloper Tools - Stop release buildstop · Ask FluxBuilder to stop the active cloud release build for a project.FluxBuilderFluxBuilderPluginrequiredDeveloper Tools
- Update design elementupdate · Patch an existing node by `_id` or dot-path; for targeted in-place edits, not broad screen redesigns.
Args:
project_id: Saved project id.
id: Node `_id` or dot-path.
object: Props to change. Merge keeps omitted keys and merges arrays by item `_id`.
mode: `merge` patches keys; `replace` overwrites props but keeps `_id`/`_schema`.FluxBuilderFluxBuilderPluginrequiredDeveloper Tools
- Update release build setupset · Configure non-sensitive release-build metadata; only passed fields change. Read setup first.
Signing files, passwords, certificates, keystores, deployment tokens, and
service credentials are deliberately not accepted by this MCP tool. Set
those in the secure FluxBuilder dashboard.
Args:
project_id: Saved project id.
bundle_id: Reverse-DNS app id, e.g. `com.yourbrand.app` (not com.example.*).
enable_onesignal: Keep a OneSignal push bundle id (default true when bundle_id set).
app_name: App display name on the home screen.
version_name: Marketing version, e.g. `1.0.0` (pass with build_number).
build_number: Build number, e.g. `1` (pass with version_name).
outputs: Targets to pre-select: any of 'apk', 'appbundle', 'ipa', 'pwa'.FluxBuilderFluxBuilderPluginrequiredDeveloper Tools
- View app designoverview · Show a project's design tree as one-level stubs.
Args:
project_id: Saved project id.
Returns:
navigation, structure, standaloneScreens; nodes include `_id`.FluxBuilderFluxBuilderPluginrequiredDeveloper Tools
- get_facilityget · Get one KIKARET facility by facility_id. Returns facility identity, official URL, and basic resolved knowledge such as prefecture, city, address, phone, and facility type.KIKARETKIKARETPluginnoneEducation
- get_facility_knowledgeget · Get resolved KIKARET knowledge for one facility. Use facility_id from search_facilities. Optionally request specific schema keys. If keys are omitted, all resolved knowledge is returned with source metadata.KIKARETKIKARETPluginnoneEducation
- pingping · Check whether the KIKARET MCP server is available.KIKARETKIKARETPluginnoneEducation
- search_facilitiessearch · Search KIKARET for childcare facilities by facility name. Use this first when you do not know the facility_id. Returns facility IDs and basic location/contact information.KIKARETKIKARETPluginnoneEducation
- Get estimateget · Retrieve detailed information for a specific estimate in a project, including its line items, customer and company details, and cost totals.magicplanmagicplanPluginrequiredOperations
- Get moisture readingsget · Retrieve the drying-measurement history recorded on a plan's restoration instruments (moisture meters and ambient sensors). Returns one entry per instrument, localized to its floor and room, with a dated series of readings: moisture content, material and relative scale, air and surface temperature, relative humidity, and humidity ratio. Instruments placed but not yet read are included with an empty readings list, so a drying-documentation gap is visible rather than the instrument disappearing. This is the built-in per-symbol measurement series that get_plan_forms does not cover. Pass a plan ID from get_project (plan_id) or get_project_plan (id).magicplanmagicplanPluginrequiredOperations
- Get plan formsget · Retrieve the workspace custom-form answers captured on a plan, grouped by the symbol instance that carries them: forms with sections and fields, each field with id, label, and typed value. Only custom forms assigned on the Forms tab are included; for the built-in moisture-measurement series use get_moisture_readings. To locate a symbol in the building, match its symbol_instance_id against the uid of the objects in get_project_plan. Pass a plan ID from get_project (plan_id) or get_project_plan (id).magicplanmagicplanPluginrequiredOperations
- Get plan statisticsget · Retrieve a plan's measurement statistics, broken down by floor and room: floor and wall areas, opening (door/window) surface areas, ceiling height, volume, and door, window, and furniture counts. Pass a plan ID from get_project (plan_id) or get_project_plan (id).magicplanmagicplanPluginrequiredOperations
- Get projectget · Retrieve details for a specific project, including its address, user, team, external reference, and plan_id.magicplanmagicplanPluginrequiredOperations
- Get project planget · Retrieve the field data recorded on the project's floor plan: the plan's custom attributes (for restoration, the claim details such as carrier, claim number, and type, category, and class of loss), and for each floor and room the entered field values and the placed objects, including equipment and affected-area annotations with their notes. Measurements are omitted here; use get_plan_statistics for areas and volumes.magicplanmagicplanPluginrequiredOperations
- Get project snapshotget · Orient on a project in one call: project details, the documentation per room (whether the room is affected by the loss, its photo count, and the number of moisture instruments placed, matched by symbol instance), and the project's estimates with status, newest first. Rooms carry affected: true only when they contain an affected-area annotation, so an affected room with photos: 0 is a likely documentation gap. A room's photo count covers photos attached to the room or an object inside it (including affected-area annotations); photos attached at floor or plan level are not room-specific and appear only in list_project_photos, so the per-room counts can sum to less than its total_count. Drill down with get_project_plan (affected areas and captured field data), get_plan_statistics (measurements), list_project_photos, get_plan_forms, get_moisture_readings, list_project_files, and get_estimate.magicplanmagicplanPluginrequiredOperations
- List estimateslist · Returns one page of estimates associated with the specified project. Each estimate carries its name, status, identifier, and timestamps, alongside a page_info block.magicplanmagicplanPluginrequiredOperations
- List project fileslist · Returns one page of files associated with a project (photos, PDFs, exports). Each file carries its id, filename, filetype, caption, a directly fetchable url, filesize, and what it is attached_to, alongside a page_info block.magicplanmagicplanPluginrequiredOperations
- List project photoslist · Returns one page of project photos (excluding exports and thumbnails). Each entry carries id, filetype, caption, a directly fetchable URL, filesize, user_created, and attached_to_uid (match against uid in get_project_plan objects). The caption is the photographer's note and is often the only description of what the photo shows. Use page to continue and page_info to navigate. Use list_project_files for non-image documents.magicplanmagicplanPluginrequiredOperations
- List projectslist · Returns one page of projects in this workspace. Filter by project name or assignee email, sort, and page through results. Each project carries its id, name, address, assignee, and timestamps, alongside a page_info block.magicplanmagicplanPluginrequiredOperations
- Workspaceget · Return the magicplan workspace this connection is authenticated as.magicplanmagicplanPluginrequiredOperations
- Generate furnituregenerate · Generates a precise, production-ready panel-based (case furniture) construction scheme.
All furniture is composed of flat sheet panels (sides, top, bottom, shelves, partitions, back panel, facades) and is intended for manufacturing or CAD/CNC processing.
STRICT DIMENSIONAL LIMITS (NON-NEGOTIABLE):
All furniture designs must fit within the following limits:
furniture_width ≤ 1800 mm;
furniture_height ≤ 2200 mm;
furniture_depth ≤ 1500 mm;
If a requested design exceeds these limits, the tool must automatically adjust the design and output compliant final dimensions.
REQUIRED PARAMETERS (MANDATORY OUTPUT):
Sheet material:
sheet_length (mm) — length of sheet material;
sheet_width (mm) — width of sheet material;
sheet_thickness (mm) — thickness of sheet material;
Overall furniture dimensions:
furniture_width (mm) — total furniture width;
furniture_height (mm) — total furniture height;
furniture_depth (mm) — total furniture depth;
Material appearance:
material_color — body material color;
material_texture — body material texture;
Facades (doors, drawers):
facade_color — facade color
facade_texture — facade texture
RULES
-Furniture must be designed exclusively from flat panels (no frames, no soft elements).
-All dimensions must be specified in millimeters.
-The construction must be realistic, logical, and manufacturable.
-Use clear, technical, unambiguous descriptions only.
-Dimensional limits must never be exceeded.
-All required parameters must be included without renaming.
OUTPUT GOAL
Generate a clear and exact furniture construction description followed by a complete list of required parameters.FlatmaFlatmaPluginnoneContent & Design
- Airport connectivityairport · How many nonstop destinations does this airport publish, and in which countries?
Returns the count of nonstop destinations from the airport's published destination table,
the number resolved to a country, the destinations by country, and the counts to the United
States, the Gulf and the UAE, with the date of the read.
This is presence, not frequency: one weekly seasonal service and a daily trunk route count
the same. It is right for "does this airport connect to the US at all" and wrong for
"how much traffic does it exchange with the US"; frequency needs a schedule source, which
this dataset is not. Seasonal and suspended routes are counted when the table lists them.
`not_covered` means the airport is outside the public set or no destination table was
parsed.
`identifier` is an IATA or ICAO code, or an airport name; a name is resolved to its code first and the answer says so in its first note. Reading the answer: `status` is resolved, unresolved or not_covered. `confidence` is verified (named on an official list or read on the operator's own page), programmatic (derived from a public structured source and only as current as that source) or unknown. `provenance` names each source behind the answer; `source_url` and `as_of` are the citation. Unknown is never filled from the airport's country or size. `not_covered` means the airport is outside the public set rather than anything about the airport; report_unmet_need records which airport was wanted.Airside Labs Aviation ToolsAirside LabsPluginrequiredData & Analytics
- Airport network integrationairport · What level of network integration does this airport have with the EUROCONTROL Network
Manager, or with the FAA's TFDM programme, as of a date?
Answers with one value: `ani` (Advanced Network Integrated: A-CDM with the higher level of
integration), `acdm` (all DPI message types sent to the network, so a TOBT and TSAT stream
exists), `adv_twr` (Advanced ATC TWR: E-DPI, C-DPI and A-DPI only, no TOBT or TSAT stream),
`tfdm_a` or `tfdm_b` (FAA Terminal Flight Data Manager configurations), `standard` (removed
from the Network Manager list by a notice), or `not_listed`. Each value carries its
definition, the notice that establishes it, the date it has held since, and the history of
changes across the notice chain.
The source is the Network Manager's own Information Notice "Updated list of A-CDM, Advanced
ATC TWR and ANI airports", parsed from each PDF in the chain from IN/25-001 (10 January 2025)
to the current one. Pass `as_of` for a historical question: Berlin Brandenburg and Barcelona
were `acdm` until 24 July 2025 and `ani` from 25 July; Edinburgh was removed on 4 December
2025 and back as `adv_twr` from 11 March 2026. Without a date you get today's list.
Do NOT read `not_listed` as "no A-CDM": the list records integration with the network, and
an airport can run a local A-CDM programme that never sends a DPI. Do NOT use this for the
airport's live status, slots or runway data, and do not infer it from airport size or a
vendor's press release; that inference is the confusion this tool exists to remove. The US
part rests on the DOT OIG report of 17 July 2024, the only public site list; the FAA names
no sites.
`identifier` is an IATA or ICAO code, or an airport name; a name is resolved to its code first and the answer says so in its first note. Reading the answer: `status` is resolved, unresolved or not_covered. `confidence` is verified (named on an official list or read on the operator's own page), programmatic (derived from a public structured source and only as current as that source) or unknown. `provenance` names each source behind the answer; `source_url` and `as_of` are the citation. Unknown is never filled from the airport's country or size. `not_covered` means the airport is outside the public set rather than anything about the airport; report_unmet_need records which airport was wanted.Airside Labs Aviation ToolsAirside LabsPluginrequiredData & Analytics
- Airport operations statusairport · Is there a positive, sourced reason this airport is not open for ordinary commercial
engagement: closed to civil traffic, in a sanctioned jurisdiction, or a general-aviation
field with no scheduled service?
Returns `closed` (no civil passenger operations), `restricted` (the airport operates but
sits under UK sanctions guidance for its jurisdiction, with the guidance page cited),
`general-aviation` (no scheduled passenger service), or `no_exception_recorded`. The last
is exactly that: this dataset records exceptions with their sources; it does not confirm
that scheduled service exists, and a `no_exception_recorded` answer should not be quoted
as "open".
Read `method` before quoting a `closed`. `sanctions-register` and the other airport-level
methods are hand-read against a named source and carry the date the airport closed: Kyiv
Boryspil since 24 February 2022, Istanbul Atatürk since April 2019. `curated-airports-type`
means the airport's own record types it as closed and usually carries no date, so it
establishes that the aerodrome is closed and not when. `general-aviation` is only ever
airport-level: the absence of scheduled service is never inferred from an airport's type
or size.
The `restricted` value can come from a country-level rule (Russia, Iran) rather than an
airport-level fact; `method` is `jurisdiction-rule` when it does, the rule is applied to
the country the airport resolves to, and it is the UK's guidance, not a statement about
any other jurisdiction's law. An airport in one of those jurisdictions that nobody has
read individually still answers `restricted` on the country rule alone, and its notes say
so. Do NOT use this for daily operational status, NOTAMs or temporary closures.
`identifier` is an IATA or ICAO code, or an airport name; a name is resolved to its code first and the answer says so in its first note. Reading the answer: `status` is resolved, unresolved or not_covered. `confidence` is verified (named on an official list or read on the operator's own page), programmatic (derived from a public structured source and only as current as that source) or unknown. `provenance` names each source behind the answer; `source_url` and `as_of` are the citation. Unknown is never filled from the airport's country or size. `not_covered` means the airport is outside the public set rather than anything about the airport; report_unmet_need records which airport was wanted.Airside Labs Aviation ToolsAirside LabsPluginrequiredData & Analytics
- Airport operatorairport · Who operates this airport, which group would take the commercial meeting, and who owns
it?
Returns operator_name, operator_group (the parent that a sales or partnership conversation
lands with, spelled consistently: Aena, MAG, Groupe ADP, Fraport, VINCI Airports, Adani and
so on), ownership_class (public-national, public-regional, private-group,
private-individual, concession-mixed or unknown), owner_name, concession_end_year where a
public page states it, and the listed parent and ticker where there is one.
Sources: Wikidata for every airport in the set (operator P137, owned by P127, parent P749),
and the operator's own website, annual report or a national register where a row is
`verified`. A `programmatic` row is only as current as its Wikidata item and can lag a
concession by years; read the note before relying on it, and prefer a verified row.
Do NOT use this to establish who owns the airport's systems or data, what vendors it uses,
or whether the group buys centrally: it is an ownership and operator fact, not a procurement
one. `not_covered` means the airport is outside the public set this dataset covers.
`identifier` is an IATA or ICAO code, or an airport name; a name is resolved to its code first and the answer says so in its first note. Reading the answer: `status` is resolved, unresolved or not_covered. `confidence` is verified (named on an official list or read on the operator's own page), programmatic (derived from a public structured source and only as current as that source) or unknown. `provenance` names each source behind the answer; `source_url` and `as_of` are the citation. Unknown is never filled from the airport's country or size. `not_covered` means the airport is outside the public set rather than anything about the airport; report_unmet_need records which airport was wanted.Airside Labs Aviation ToolsAirside LabsPluginrequiredData & Analytics
- Airport size bandairport · Which ACI passenger size band is this airport in, on its latest published annual total?
Returns the passengers figure, its year, and the band on ACI World's published ASQ size
categories: under 2M, 2 to 5M, 5 to 15M, 15 to 25M, 25 to 40M, over 40M passengers per year.
The figure comes from the airport's Wikipedia infobox (its latest published annual total),
not from ACI, so airports are not on a common year; the year travels with the answer, and a
figure dated the current year is flagged as probably part-year.
Use it to say "a 25 to 40M airport" in the way the industry does. Do NOT use it as a traffic
statistic for comparison across airports or years, for growth, or for anything that needs a
consistent reporting basis; that needs a statistics source. `not_covered` means the airport
is outside the public set or its article carries no passenger figure.
`identifier` is an IATA or ICAO code, or an airport name; a name is resolved to its code first and the answer says so in its first note. Reading the answer: `status` is resolved, unresolved or not_covered. `confidence` is verified (named on an official list or read on the operator's own page), programmatic (derived from a public structured source and only as current as that source) or unknown. `provenance` names each source behind the answer; `source_url` and `as_of` are the citation. Unknown is never filled from the airport's country or size. `not_covered` means the airport is outside the public set rather than anything about the airport; report_unmet_need records which airport was wanted.Airside Labs Aviation ToolsAirside LabsPluginrequiredData & Analytics
- Data requirementsdata · What data does this use case, role or organisation type need, and how fresh?
Three modes. With `use_case_id`: that use case's requirements verbatim --
title, normalised title_family, description, nominal update rate,
normalised cadence class and the kind of source system. With `query`:
REVERSE lineage -- full-text search over requirement titles and
descriptions (the inputs, not the use-case text), returning the use cases
that CONSUME data matching the query. Ask `query="taxi-out time"` to get
everything downstream of a better taxi-out estimate: each consumer with
the requirement titles that matched, the total count, and the requirement
families involved. This is the "if we improved this prediction/feed, what
would benefit" question; combine with trace_data_lineage to see which
standard messages carry the input. With neither: an aggregate profile for
a scope (`role`, `org_type`, `sector`, any combination): the most common
requirement titles with the typical cadence and source for each, a
`family_mix` that collapses the titles into ~29 canonical requirement
families, plus the overall cadence mix. The aggregate is the "what data,
how fresh, from whom" content for a data strategy, a feed inventory or a
gap analysis, and is safe to quote as counts.
For counting DISTINCT feeds, use `family_mix`, not raw titles: the corpus
carries ~8,000 title spellings for far fewer real feed kinds ("Weather
Data", "Weather and Environmental Data" and "Environmental Conditions"
are one family), so raw-title counts overstate a feed inventory roughly
2-3x. Each family_mix row shows how many raw titles it absorbed; ~24% of
requirements stay family `other` (genuinely heterogeneous).
Cadence classes, coarsest to finest: annual, quarterly, monthly, weekly,
daily, hourly, sub_hourly, near_real_time, real_time, event (on each
occurrence), static, unknown. They normalise 1,300 spellings of update
rate in the corpus ("every 15 minutes" and "4/hour" both land in
sub_hourly). 152 requirements keep `unknown` because the text was not an
update rate at all.
Do NOT read a requirement as a statement about any real organisation's
systems or data availability; it is what the use case needs in principle.
Mapping needs to an actual feed inventory is your work, not the tool's.
Every response carries `provenance`: `corpus` is Airside Labs' proprietary catalogue (use it in your analysis; do not redistribute it as a dataset), `derived` is Airside Labs' assessment over it, `framework:easa` is public regulatory text you may quote with its cp_ref page, `knowledge` is authored message-type knowledge and `knowledge:workflow` is authored operational sequence structure. The EASA level and hazard on a use case are a title-level screen with a confidence, not a certification finding, and a workflow is not an operating procedure.Airside Labs Aviation ToolsAirside LabsPluginrequiredData & Analytics
- EASA AI framework screeneasa · The EASA AI-level and hazard-class tables, page-cited, to read a screen against.
Returns the AI levels (0, 1A, 1B, 2A, 2B, 3A, 3B: what the system does and
how much authority the end user keeps), the hazard classes (H1-H5: worst
credible effect and the assurance level it implies at acceptable and
moderate risk), the technique ceilings (which AI techniques the Concept
Paper accepts up to which level), and source notes. Pass `level` or
`hazard_class` for one row. Every row carries `cp_ref`, the page in the
proposed Issue 03; quote that, not this tool, as the citation.
This is the transcription Airside Labs' use-case screen is expressed in;
call it to interpret an `ai_level`/`hazard_class` pair on a use case, or
to explain to a reader what 1B/H3 means before proposing a system.
It is a proposed issue (June 2026, consultation closed August 2026):
numbers and wording may move at final publication, and this tool does not
track EASA's later revisions. Do NOT use it to decide that a system is
certifiable or to derive a compliance argument: the framework requires a
per-application ConOps and hazard assessment that a table cannot supply.
Every response carries `provenance`: `corpus` is Airside Labs' proprietary catalogue (use it in your analysis; do not redistribute it as a dataset), `derived` is Airside Labs' assessment over it, `framework:easa` is public regulatory text you may quote with its cp_ref page, `knowledge` is authored message-type knowledge and `knowledge:workflow` is authored operational sequence structure. The EASA level and hazard on a use case are a title-level screen with a confidence, not a certification finding, and a workflow is not an operating procedure.Airside Labs Aviation ToolsAirside LabsPluginrequiredData & Analytics
- Feedback statusfeedback · What happened to a suggestion or unmet-need report filed here? No API key is needed.
Pass the `fingerprint` from a submit_suggestion or report_unmet_need receipt (12 hex
characters). Returns `status`: `shipped` (a person acted on it and the change is live, with
what shipped and when), `declined` (read and not taken up, with the reason), `acknowledged`
(read, still open) or `open` (recorded, not yet reviewed), or `unknown_fingerprint` if
nothing was ever filed under it. `times_reported` says how many times that exact text has
been filed, and `first_reported` / `last_reported` when.
Resolutions are written by a person when something ships, so `open` means exactly that: it
has not been read yet, or has been read and not written up. Filing again does not move it.
Do NOT infer from `shipped` that the whole class of problem is fixed: the note says what was
changed and the dataset build it landed in.Airside Labs Aviation ToolsAirside LabsPluginrequiredData & Analytics
- Get use caseget · The full record for one use case, by id from a search or landscape result.
Returns the use-case text; the role that owns it with its description; the
organisation type (canonical and as the corpus names it); every data
requirement -- title, description, nominal update rate with a normalised
cadence class (real_time … annual), and the kind of system it typically
comes from; the EASA screen (AI level, hazard class, confidence, rationale)
with the framework rows it points at, page-cited; keywords; and the five
nearest use cases by text similarity.
Use this for the few worked examples an argument actually needs, chosen
with search_use_cases, use_case_landscape or similar_use_cases first.
Calls are counted against your key's daily allowance, so walking the
catalogue record by record is the wrong tool: use the aggregates.
The data requirements are what this use case *needs*, stated generically
("airport operational database", "ADS-B feed"), not what any particular
airport has. A requirement with cadence_class `unknown` had an update rate
the normaliser could not read; the raw `update_rate` is still there.
Do NOT treat the EASA screen as a determination: it is a title-level
assessment by Airside Labs with a confidence, made to sort a portfolio,
and a real classification needs a ConOps. Cite the framework rows by their
cp_ref if you quote them.
Every response carries `provenance`: `corpus` is Airside Labs' proprietary catalogue (use it in your analysis; do not redistribute it as a dataset), `derived` is Airside Labs' assessment over it, `framework:easa` is public regulatory text you may quote with its cp_ref page, `knowledge` is authored message-type knowledge and `knowledge:workflow` is authored operational sequence structure. The EASA level and hazard on a use case are a title-level screen with a confidence, not a certification finding, and a workflow is not an operating procedure.Airside Labs Aviation ToolsAirside LabsPluginrequiredData & Analytics
- Parse flight identifierparse · Split a flight designator into carrier and number, and identify the carrier.
Accepts the forms that arrive in real message traffic: TK1979, BAW276,
"U2 8341", BA02490, BA2490A. Returns the parsed carrier with its resolved
airline, the flight number, any operational suffix, which scheme was used
(IATA or ICAO), and both normalised forms.
Two things this tool will not do. It will not tell you who operated the
flight: a designator names the marketing carrier, and a codeshare is
invisible in the string. And it will not invent a carrier for a bare flight
number -- pass `context` and it will look for an identifier in that text,
but the result is capped at 0.6 confidence and returned as a candidate,
because inference is not identification.
Pass `date` when parsing historical data: the carrier depends on it, since
SN2103 was a Sabena flight in 1995 and a Brussels Airlines flight in 2020.
A string that parses correctly but names no known airline returns
`unresolved` with a note saying exactly that -- the string was read and
nothing was identified, which are different achievements. Where a
two-character designator matches nothing but its reverse does, the reverse
is offered as an alternate and never as the answer.
Reading `confidence`: 1.0 means an exact unique match on an unambiguous identifier for the as_of date. 0.7-0.99 means a unique match reached through normalisation, an alias or a historical record. 0.4-0.69 means the best of several plausible candidates and `alternates` is populated -- prefer asking the user over picking one. Below 0.4 is speculative: do not act on it.
`status` is resolved, ambiguous or unresolved. An unresolved answer is a real result, not an error: it means this dataset cannot identify the thing, and inventing one would be worse. Every field in `best` has a citation in `provenance`.Airside Labs Aviation ToolsAirside LabsPluginrequiredData & Analytics
- Report unmet needreport · Tell us what you came looking for and could not get from these tools.
Call this when you needed something aviation-related that this toolset did
not give you. It is not an error channel and it is not a retry: it is how
the next version of these tools learns what is missing. A need reported
here is read by a person.
Use it when:
- a tool returned `unresolved` and you believe the thing exists
- a tool returned `ambiguous` and nothing available could break the tie
- the entity resolved but a field you needed was null or absent
- no tool here covers the question at all
- a tool answered confidently and the answer looked wrong
`gap_kind` must be one of: `unresolved`, `ambiguous`, `missing_field`,
`no_tool`, `wrong_answer`, `no_use_case` (the use-case catalogue had
nothing on the operational need you searched for).
`sought` is the important field: say what you were trying to find, in your
own words, as specifically as you can. "The wake turbulence category for
A21N" is useful. "Aircraft data" is not.
Do NOT use this instead of answering the user. Report the gap, then tell
the user plainly that you could not find it — filing this does not get you
an answer, now or in this conversation.
Do NOT paste conversation history, personal data or customer identifiers
into it. Send the shape of the need, not the payload: what field, about
what kind of entity, for what purpose. Text is length-capped and the log is
treated as sensitive, but the cheapest way to keep data out of it is not to
send it.
Returns a receipt: whether it was recorded, a fingerprint, and how many
times this same need has been reported before — which tells you whether you
have found something known or something new.Airside Labs Aviation ToolsAirside LabsPluginrequiredData & Analytics
- Resolve aircraft typeresolve · Which aircraft type does this designator or marketing name refer to?
Accepts an ICAO type designator (A21N) or a marketing name people actually
say ("A321neo", "Dash 8-400", "777-300ER", "Q400"). Returns the ICAO
designator, manufacturer, model, engine count and type, aircraft class, and
the ICAO wake turbulence category.
A name that identifies a family rather than a variant -- "Dreamliner",
"777X", "A330neo" -- returns `ambiguous` with the variants as alternates
and a confidence in the 0.4-0.69 band. That is the correct answer to an
imprecise question; do not collapse it to the first alternate.
`wtc` (wake turbulence category) is stated for almost every type, from FAA
Order JO 7360.1K, and is cited like any other field. Where it is null the
document leaves it blank -- do NOT fill it in from your own knowledge if the
caller needs it for separation or charging, say it is unavailable.
IATA aircraft type codes are NOT held -- the three-character form used in
schedules and booking systems, "77W" or "32N". IATA's list is licensed and
is not reproduced here, so those return `unresolved` with a note saying so.
If you are working from schedule data, convert to the ICAO designator
before calling (B77W, A20N); if you cannot, report the mapping as
unavailable rather than guessing it. `typical_seats_range` is null because
seat count is an operator configuration rather than a property of the type.
Do NOT use this to determine what type operated a particular flight; use
resolve_registration for a specific airframe.
Reading `confidence`: 1.0 means an exact unique match on an unambiguous identifier for the as_of date. 0.7-0.99 means a unique match reached through normalisation, an alias or a historical record. 0.4-0.69 means the best of several plausible candidates and `alternates` is populated -- prefer asking the user over picking one. Below 0.4 is speculative: do not act on it.
`status` is resolved, ambiguous or unresolved. An unresolved answer is a real result, not an error: it means this dataset cannot identify the thing, and inventing one would be worse. Every field in `best` has a citation in `provenance`.Airside Labs Aviation ToolsAirside LabsPluginrequiredData & Analytics
- Resolve airlineresolve · Which airline does this designator, name or callsign refer to on a date?
Accepts an IATA two-character designator, an ICAO three-letter designator,
an airline name or a callsign. Returns the operator with both designators,
country, operational status and, where an airline ceased, its successor.
`as_of` matters more here than for any other tool. IATA two-character
designators are heavily reused: SN was Sabena until 2001 and has been
Brussels Airlines since 2007, so "SN" without a date is a question with two
answers. Where a date falls between two holders the tool returns
`unresolved` with both as alternates rather than guessing. ICAO three-letter
designators are not recycled the same way and are the safer identifier to
carry through a pipeline.
`status` is reported as at the date asked about, not as at today: asked
about 2005, Northwest Airlines is `active`, with a note that it merged into
Delta in 2010. A status of `unknown` means no reliable source states it --
the bulk open data flags long-dead airlines as active, so it is not
trusted. Unknown is not evidence of ceasing.
Do NOT use this for fleet lists, routes, schedules, alliance membership or
financial status.
Reading `confidence`: 1.0 means an exact unique match on an unambiguous identifier for the as_of date. 0.7-0.99 means a unique match reached through normalisation, an alias or a historical record. 0.4-0.69 means the best of several plausible candidates and `alternates` is populated -- prefer asking the user over picking one. Below 0.4 is speculative: do not act on it.
`status` is resolved, ambiguous or unresolved. An unresolved answer is a real result, not an error: it means this dataset cannot identify the thing, and inventing one would be worse. Every field in `best` has a citation in `provenance`.Airside Labs Aviation ToolsAirside LabsPluginrequiredData & Analytics
- Resolve airportresolve · Which aerodrome does this airport code or name refer to on a given date?
Accepts an IATA three-letter code, an ICAO four-letter code, an airport
name fragment or a city name. Returns the aerodrome with its codes,
location, elevation, IANA timezone and type (large, medium, small,
heliport or closed).
Pass `as_of` whenever the question concerns a past date. Airport codes move
between aerodromes: ATH meant Ellinikon until 2001 and Athens
International after it; HKG meant Kai Tak until 1998. Without a date these
questions get today's answer, which is silently wrong for historical data.
`timezone` is always an IANA identifier such as Europe/London, never a UTC
offset, because an offset cannot express daylight saving.
Do NOT use this for live operational status, runway or stand data, slots,
or whether an airport is currently accepting traffic. It answers identity
questions only. A name fragment matching several aerodromes returns
`ambiguous` with candidates rather than picking one; use `country_hint`
(ISO 3166 alpha-2) to narrow it.
Reading `confidence`: 1.0 means an exact unique match on an unambiguous identifier for the as_of date. 0.7-0.99 means a unique match reached through normalisation, an alias or a historical record. 0.4-0.69 means the best of several plausible candidates and `alternates` is populated -- prefer asking the user over picking one. Below 0.4 is speculative: do not act on it.
`status` is resolved, ambiguous or unresolved. An unresolved answer is a real result, not an error: it means this dataset cannot identify the thing, and inventing one would be worse. Every field in `best` has a citation in `provenance`.Airside Labs Aviation ToolsAirside LabsPluginrequiredData & Analytics
- Resolve registrationresolve · Which airframe wore this registration on a given date?
Accepts a tail number in any common form: VH-OQA, VHOQA, lowercase, padded
with whitespace. Returns the aircraft with its country of registry, ICAO
type designator, an embedded resolved aircraft type, operator, owner,
serial number and Mode-S hex address.
A registration is a slot rather than a permanent name: marks are
surrendered and reissued. VP-BRZ was an Aeroflot A320 until 2017 and a
different airframe entirely from 2019, with a different Mode-S address.
Pass `as_of` for any historical question. Where the date falls between two
holders the tool returns `unresolved`, reports the country from the
nationality mark, and offers both airframes as alternates.
When the tail is unknown but the nationality mark is recognised, the
response carries a `partial` object with the country of registry at
confidence around 0.5. That is a partial answer, not a resolution: the
aircraft is still unidentified.
Coverage is not global. An unresolved registration means this dataset does
not hold it, NOT that the aircraft does not exist. Do NOT infer that an
aircraft is fictitious from an unresolved result, and do NOT use this for
airworthiness, lease status or current position.
Reading `confidence`: 1.0 means an exact unique match on an unambiguous identifier for the as_of date. 0.7-0.99 means a unique match reached through normalisation, an alias or a historical record. 0.4-0.69 means the best of several plausible candidates and `alternates` is populated -- prefer asking the user over picking one. Below 0.4 is speculative: do not act on it.
`status` is resolved, ambiguous or unresolved. An unresolved answer is a real result, not an error: it means this dataset cannot identify the thing, and inventing one would be worse. Every field in `best` has a citation in `provenance`.Airside Labs Aviation ToolsAirside LabsPluginrequiredData & Analytics
- Search use casessearch · Which aviation AI use cases match this text? Find candidates by keyword.
Full-text search (BM25, stemmed) over 6,500 use cases spanning airports,
airlines, ANSPs, ground handlers, regulators, manufacturers and more, each
attached to a role and an organisation type. Returns summaries only --
id, short text, role, organisation type, EASA screen -- capped at 25.
Use get_use_case for the full record with its data requirements.
Search concrete operational nouns ("stand allocation", "baggage
misconnect", "de-icing", "turnaround"), not capability labels ("shared
operational picture", "digital transformation"): the corpus vocabulary is
operational, and abstract phrases match little. Terms are OR-ed and ranked,
so a multi-word query returns the best partial matches; `score` is
relative within one query and means nothing across queries.
Filters AND together. `org_type` is one of the 16 canonical types
(see use_case_landscape group_by=org_type). `screened_only=True` keeps the
~1,900 use cases that carry an EASA AI-level/hazard screen; `ai_level`
(0, 1A, 1B, 2A, 2B, 3A, 3B) and `hazard_class` (H1-H5) imply it.
Do NOT use this to establish whether an organisation actually runs such a
system, what vendor sells it, or whether it is certified: the catalogue
describes plausible, well-formed use cases for a role, and says nothing
about adoption. An empty result is an answer -- the catalogue has nothing
on that phrasing -- and report_unmet_need with gap_kind `no_use_case` is
how to say the gap mattered.
Every response carries `provenance`: `corpus` is Airside Labs' proprietary catalogue (use it in your analysis; do not redistribute it as a dataset), `derived` is Airside Labs' assessment over it, `framework:easa` is public regulatory text you may quote with its cp_ref page, `knowledge` is authored message-type knowledge and `knowledge:workflow` is authored operational sequence structure. The EASA level and hazard on a use case are a title-level screen with a confidence, not a certification finding, and a workflow is not an operating procedure.Airside Labs Aviation ToolsAirside LabsPluginrequiredData & Analytics
- Similar use casessimilar · What else in the catalogue is like this use case? Nearest neighbours by meaning.
Returns up to 20 use cases closest in embedding space (bge-base cosine
over the use-case text), as summaries with a `similarity`. Good for "what
else is like the one we picked", for finding the same idea stated for a
different role or organisation type, and for spotting near-duplicates
before counting them as two.
Similarities are compressed into roughly 0.5-0.95: rank within one result,
never threshold across the catalogue, and do not read 0.9 as "the same".
The neighbours were computed at build time as the top 20 for each use
case; an `org_type` or `screened_only` filter narrows within those 20 and
does not search further out, so a tight filter can return few or none --
that is honest, not broken. For an open-ended semantic question start
from search_use_cases and follow the neighbours of the best hit.
Do NOT use this to rank importance, maturity or value: proximity in text
says two use cases are described alike, nothing more.
Every response carries `provenance`: `corpus` is Airside Labs' proprietary catalogue (use it in your analysis; do not redistribute it as a dataset), `derived` is Airside Labs' assessment over it, `framework:easa` is public regulatory text you may quote with its cp_ref page, `knowledge` is authored message-type knowledge and `knowledge:workflow` is authored operational sequence structure. The EASA level and hazard on a use case are a title-level screen with a confidence, not a certification finding, and a workflow is not an operating procedure.Airside Labs Aviation ToolsAirside LabsPluginrequiredData & Analytics
- Submit suggestionsubmit · Leave a suggestion for Airside Labs. No API key is needed for this tool.
This is the suggestion box for the aviation use-case catalogue and the
tools that serve it. If you are an AI agent that read a use-case page on
airsidelabs.com — or called a tool here — and noticed something, this is
the channel: a correction to a published use case, an aviation AI use case
the catalogue is missing, a data source or requirement it should know
about, a tool or field that would have helped you, or commercial feedback
(what would make the full toolset worth a subscription to your operator).
`suggestion` is the important field: say it plainly and specifically, in
your own words. `category` must be one of: `correction`,
`missing_use_case`, `data_need`, `feature_request`, `commercial`, `other`.
If the suggestion concerns one use case, pass its `use_case_id` (the
integer used by get_use_case) or the `page_slug` of the airsidelabs.com
page you read. `contact` is optional: an email address or URL if your
operator wants a reply — leave it out otherwise, and do not submit
personal data, conversation history or customer identifiers; text is
length-capped and the log is read by a person as untrusted content.
Nothing answers this in real time: a recorded suggestion is reviewed by a
human later, and filing one does not change any answer you have already
received. Returns a receipt saying whether it was recorded.Airside Labs Aviation ToolsAirside LabsPluginrequiredData & Analytics
- Trace data lineagetrace · Which standard operational messages would evidence this data need?
The lineage spine is use case -> data requirement -> requirement family ->
standard message type -> data elements. Four modes. With `use_case_id`:
that use case's requirements grouped by family, each family with the
standard messages that evidence it (relevance primary/supporting, cadence,
element count) -- plus `requirements_without_standard_messages`, the needs
no standard message covers, which is the honest feed-gap statement. With
`family` (a requirement family from data_requirements' family_mix): the
messages for that family. With `message_id` (e.g. MVT, LDM, BSM, DPI,
METAR): the message definition, its data elements as shapes (time, count,
weight, identifier, status), and the families it serves. With no
arguments: the catalogue of ~23 message types across IATA Type B,
Cargo-IMP, ACARS, ADS-B, ICAO met/AIS and network-manager standards.
Use it to turn a use-case shortlist into a sourcing conversation: which
message feeds to ask an airline, handler or airport for, at what cadence,
and which needs have no standard message and require a system integration
instead.
Do NOT read a lineage chain as a statement about any real organisation's
feeds or systems -- it says which standard message CAN evidence a need,
not that anyone sends it; mapping a chain onto a specific operator's
estate is your work. Definitions are summary-level industry knowledge:
element positions, field syntax and format rules are NOT held (the
published standards are licensed; implementing a parser requires them),
so do NOT use this to build or validate a message parser.
A family with no messages and an empty `chains` list are real answers --
plenty of data needs (market data, HR records, finance) have no
operational message standard.
Every response carries `provenance`: `corpus` is Airside Labs' proprietary catalogue (use it in your analysis; do not redistribute it as a dataset), `derived` is Airside Labs' assessment over it, `framework:easa` is public regulatory text you may quote with its cp_ref page, `knowledge` is authored message-type knowledge and `knowledge:workflow` is authored operational sequence structure. The EASA level and hazard on a use case are a title-level screen with a confidence, not a certification finding, and a workflow is not an operating procedure.Airside Labs Aviation ToolsAirside LabsPluginrequiredData & Analytics
- Trace operational workflowtrace · What happens before and after this use case, and who hands off to whom?
The catalogue is otherwise flat -- role, use case, data requirement -- with
no edges. This is the edges: named operational sequences with their steps in
order, the role at each step, the `gate` that must hold before the next one
may start, and what passes between roles at each handoff (headset, radio,
hand signal, system, visual, document, verbal, formal correspondence).
Three modes. With `use_case_id`: every workflow that places that use case,
its position, and the steps immediately `preceded_by` and `followed_by` it
with what transfers. That is the real operational dependency -- distinct
from `similar_use_cases`, which is text similarity, and from
`data_requirements(query=...)`, which infers a relationship from two use
cases sharing a feed. A gate is a dependency; a shared feed is a
correlation. With `workflow_id`: the whole sequence end to end. With
`phase` (arrival, turnaround, departure, abnormal, oversight,
certification, crew qualification, planning, mission) or nothing: the
catalogue of workflows with their step and handoff counts.
Use it to answer "if this prediction improved, what downstream step benefits
and who would have to act on it", to see which roles a change touches, and
to find the abnormal branches -- communication failure, coupling break-off,
spillage, a ramp finding serious enough to stop the aircraft -- which are
sequences in their own right and where the costly failures live. Some
sequences cross an organisation: a finding travels from an authority to an
operator and back, and those edges carry the channel `formal
correspondence`.
Coverage is ground handling, regulatory oversight and certification, crew
qualification and rostering, and specialised missions -- not the whole
catalogue. A use case with an empty `placed_in` is outside the covered
sequences, which is a statement about coverage and not about the use case,
and some are unplaced because they are standing monitoring states rather
than sequences. A step whose `role_in_catalogue` is false is a real
participant the catalogue holds no use cases for -- the flight deck on a
turnaround, the officer who signs a certificate -- not a data error. The
sequences are Airside Labs' derived structure: they say how this work is
ordered in the industry, NOT that any particular operator runs it this way,
and they are not an operating procedure. Do NOT use one as a checklist to
work to; the authority for that is the operator's own manual.
Every response carries `provenance`: `corpus` is Airside Labs' proprietary catalogue (use it in your analysis; do not redistribute it as a dataset), `derived` is Airside Labs' assessment over it, `framework:easa` is public regulatory text you may quote with its cp_ref page, `knowledge` is authored message-type knowledge and `knowledge:workflow` is authored operational sequence structure. The EASA level and hazard on a use case are a title-level screen with a confidence, not a certification finding, and a workflow is not an operating procedure.Airside Labs Aviation ToolsAirside LabsPluginrequiredData & Analytics
- Use-case landscapeuse · How many use cases are there, sliced one way? Counts you can quote.
`group_by` is one of org_type (16 canonical organisation types: Airport,
Airline, Air Navigation Service Provider, Ground Handling, Regulator /
Authority, Aerospace Manufacturing, MRO / Maintenance …), sector, role
(500+ roles), ai_level, hazard_class, or cadence_class (counts data
requirements rather than use cases). Filters AND together and apply
before grouping, so group_by=role with org_type=Airport lists airport
roles by how many use cases each carries. Also returns the total in scope
and how many of those carry an EASA screen.
This is the tool to call first: it tells you what the catalogue covers
before you search it, and the exact spellings that the filters accept.
Grouping the whole catalogue by ai_level or hazard_class shows a large
null bucket -- only the ~1,900 airport-operations use cases were screened;
pass screened_only=True to see the screened distribution alone.
Counts describe the catalogue, not the industry: a type with many use
cases is a type the corpus describes in detail, not one that adopts more
AI. Do NOT present a count as market evidence.
Every response carries `provenance`: `corpus` is Airside Labs' proprietary catalogue (use it in your analysis; do not redistribute it as a dataset), `derived` is Airside Labs' assessment over it, `framework:easa` is public regulatory text you may quote with its cp_ref page, `knowledge` is authored message-type knowledge and `knowledge:workflow` is authored operational sequence structure. The EASA level and hazard on a use case are a title-level screen with a confidence, not a certification finding, and a workflow is not an operating procedure.Airside Labs Aviation ToolsAirside LabsPluginrequiredData & Analytics
- Validate identifiersvalidate · Do these aviation identifiers describe the same thing on this date?
Give any combination of a registration (tail number), a Mode-S 24-bit
address, an ICAO type designator, an operator name, an airline designator
(IATA or ICAO), a callsign and a flight designator, plus `as_of`. Each
pair that can be checked is checked, and every check comes back with a
verdict -- `consistent`, `contradicted` or `unverifiable` -- the detail,
and the rule that decided it. `contradictions` lists the failures on
their own so an agent can act on them without reading everything.
The top-level verdict is `consistent` ONLY when every check verified.
When some checks passed but others could not be checked it is
`consistent_where_checkable` -- a different answer, because an absent
record silences exactly the checks that would catch a false claim about
that airframe. `unverifiable_count` says how many checks were silent;
treat anything above zero as partial coverage, not a pass.
Use it before acting on identifiers assembled from more than one message:
a movement message's registration against a surveillance track's address,
a schedule's flight number against the operating airline, a type in a
load message against the airframe's record. One contradiction is a
contradiction; the top-level `verdict` never averages it away against
the checks that passed.
What the rules are: the registration record on the date (address, type,
operator); a *previous* holder of a re-issued mark is named as such rather
than reported as a mismatch; the FAA allocation arithmetic for N-numbers
(an address decodes to exactly one N-number, so a wrong pairing is provable
without any second source); a military or government airframe is said,
not judged; the airline's callsign against its record and the FAA
contractions order; the carrier inside the flight designator against the
airline given; and whether the type designator exists at all.
`unverifiable` means this dataset cannot say -- there is no record, or the
record lacks the field -- and is a different answer from `consistent`. Do
NOT read it as a pass. It does not check hex-to-country for non-US
addresses, performance plausibility (range, block time) or anything live,
and it does not resolve identifiers you did not give it: call the
resolve_* tools for that. Pass `as_of` for any historical question; marks
and designators are reused and an undated check is silently wrong for
past data.Airside Labs Aviation ToolsAirside LabsPluginrequiredData & Analytics
- Check for scam signalscheck · Check a phone number, message, email address, link, or website with IsThisSpam before advising the user to call, click, pay, or share information. Returns a cautious, evidence-led verdict; no result guarantees safety.IsThisSpamIsThisSpamPluginnoneSecurity
- CogniFit Health Checkcognifit · Check the CogniFit API health endpoint.CogniFitCogniFitPluginrequiredHealth & Wellness
- Get Account Contextget · Return account context that requires authentication to call.
Authentication is handled via HTTP Bearer token. The auth_token parameter
is only for testing in tools that cannot set HTTP headers.CogniFitCogniFitPluginrequiredHealth & Wellness
- Get Cognitive Profileget · Get the current cognitive profile snapshot of the user based on their most recent session.
Two possible responses when the user is authenticated:
1. User has completed at least one session — returns {success: true, cognitiveProfile: {...}, baseScore: 800, targetScore: int}
cognitiveProfile contains:
- sessionCompleted: total number of sessions done
- lastSessionDate: date of the most recent session (YYYY-MM-DD HH:MM:SS)
- lastSessionKey: typeKey of the last session (e.g. NORMAL, GENERAL, MENTAL_ARITHMETIC_ASSESSMENT)
- lastSessionType: "ASSESSMENT" or "TRAINING"
- cognitiveAge: {age: int, precision: "EXACT" | "APPROXIMATE"}
- score: overall cognitive score (0–800); higher is better. baseScore is always 800.
- skills: list of {key, value} pairs for 23 individual cognitive skills (0–800 each), such as
INHIBITION, SHIFTING, UPDATING, WORKING_MEMORY, PLANNING, RESPONSE_TIME, etc.
- categories: list of {key, value} pairs for 5 broad domains (0–800 each):
REASONING, MEMORY, ATTENTION, MOTOR, PERCEPTION
2. User has no sessions yet — returns {error: 1100, errorMessage: "User has not completed any assessment or training session yet"}
In this case, encourage the user to start their first assessment to get their cognitive profile.
Use this data to summarise the user's current cognitive state, highlight strong and weak areas,
and offer to start a new training or assessment session to improve their scores.
These scores measure performance on cognitive tasks. They are not a clinical assessment:
never read them as evidence of a disorder, impairment, or diagnosis, and if the user is
worried about what they mean, suggest they discuss it with a healthcare professional.
IMPORTANT: Before calling this tool, call get_secure_account_context and verify
that 'user_type' is NORMAL_USER. If not, inform the user that he has no access to it.
Args:
auth_token: Injected by middleware, do not set manually.CogniFitCogniFitPluginrequiredHealth & Wellness
- Get Historical Cognitive Profileget · Get the full historical cognitive profile of the user across all past sessions.
Returns a list of sessions ordered from most recent to oldest. Each session includes:
- sessionId: unique session identifier
- date: when the session took place (YYYY-MM-DD HH:MM:SS)
- type: either "ASSESSMENT" (measures current cognitive state) or "TRAINING" (practice session)
- typeKey: the specific assessment variant (e.g. GENERAL, MENTAL_ARITHMETIC_ASSESSMENT,
VERBAL_FLUENCY_TASK_ASSESSMENT, NORMAL for training)
- cognitiveAge: object with "age" (integer) and "precision" ("EXACT" or "APPROXIMATE")
- score: overall cognitive score (0–800); higher is better. baseScore is always 800.
- skills: list of {key, value} pairs for 23 individual cognitive skills (0–800 each), such as
INHIBITION, SHIFTING, UPDATING, WORKING_MEMORY, PLANNING, RESPONSE_TIME, etc.
- categories: list of {key, value} pairs for 5 broad domains (0–800 each):
REASONING, MEMORY, ATTENTION, MOTOR, PERCEPTION
- questionsAndAnswers (ASSESSMENT only): health questionnaire answers collected at assessment time
(glasses, handedness, physical/mental/social health, lifestyle habits, etc.)
Also returns:
- baseScore: 800 (the maximum possible score)
- targetScore: the personalised score goal for this user
Use this data to explain cognitive evolution over time, highlight strengths and weaknesses,
compare assessment vs training sessions, and encourage the user to keep training.
These scores measure performance on cognitive tasks. They are not a clinical assessment:
never read them or their trend as evidence of a disorder, impairment, or diagnosis, and if
the user is worried about what they mean, suggest they discuss it with a healthcare
professional.
Args:
auth_token: Injected by middleware, do not set manually.CogniFitCogniFitPluginrequiredHealth & Wellness
- Get Product Catalogget · Return CogniFit product metadata used by chats.
HEALTH CLAIMS: some entries are named after a clinical population and their
descriptions use therapeutic language — stroke, Parkinson's, ADHD, depression,
fibromyalgia, chemotherapy and others. Present them as what they are: cognitive
stimulation programmes designed with that population in mind. Never present one
as medical treatment, therapy, or a substitute for professional care, and never
tell a user a programme will treat, cure, or relieve a condition. Do not suggest
a diagnosis from anything the user says, and do not read a cognitive score as a
medical finding. If a user raises a health condition of their own, suggest they
discuss it with a healthcare professional.
Args:
family: One of "all", "training", "assessment", or "game".CogniFitCogniFitPluginrequiredHealth & Wellness
What is Tool Explorer?
Tool Explorer indexes the callable tool names and descriptions attached to public registry profiles. It is useful for seeing what agents can actually invoke, not just which profile exists.
How do category and verb filters work?
Category filters use the live registry category rollup. Verb filters use the public tool insights rollup, so the page stays backed by the same read models as the tracker charts.
Why do auth requirements matter?
Auth requirements show whether a tool is likely usable without account connection, requires authentication, is private, or is unknown in the current snapshot.