FactIQ

List the data sources FactIQ has, plus the shared table structure. Call this once before exploring. Returns the visible schemas (organization + country), the ones with no data loaded yet (`schemas_without_data` — skip them), a per-schema dataset index, and the shared DDL for the `series`, `data_points`, `dimensions` and `compound_series` tables. schemas: optional comma-separated filter (e.g. "bls,bea"). full: heavy per-dataset descriptions instead of the compact index.

get_data_catalog

Full metadata for one dataset: topic, methodology, discontinuities, release dates, any base-change notice, plus the live dimensions and example series. Call after `search_datasets` narrows you to a dataset.

describe_dataset

Recent market data the warehouse lags on: quotes, OHLCV, company profiles, FX, and commodities. Set data_type to price_history for stocks, currency pairs, and commodities; the asset format determines which market is queried. Use search_company_filings for financial statements and reported facts.

get_market_data

Get satellite-derived indicators aggregated over a country, one state/province, or a bounding box. Use for fires, emissions-based activity, rainfall, vegetation, temperature, and soil-moisture questions. For a sub-national area, call one state/province at a time. Informal regions such as "north India" or "the Midwest" have no boundary, so cover them with one call per state or a bounding box; using the whole country answers a different question. Grid cells and fire detections are activity snapshots, never fire perimeters. For ndvi_s2 and *_tropomi grids, drop or flag cells with low valid_obs_share. Results include units, source attribution, and caveats; cite the attribution. External-provider calls can take about 30 seconds, and rainfall or grid calls can take longer.

get_geo_data

Fetch one series' data: timeseries, tabular, or COMPOUND:: all work. Returns title, units, frequency, notes and data points, with source metadata. At most 50 points come back; if the series is longer the result is flagged `"truncated": true` — narrow it with `from_year`/`to_year`, or pull the series through `run_sql` with an aggregation (e.g. monthly/quarterly via `date_trunc`) rather than trying to fetch every point.

get_series

Render a FactIQ-styled interactive chart inline, from data you have already fetched. Before calling, finish all data retrieval, calculations, source reconciliation, and title/value checks. For each intended chart, call this tool exactly once, only after its data and title are final; never render a draft and then re-render a correction. Build the final numbers from run_sql / get_series, aggregated to chart resolution. - x_values: the x axis — ISO dates ("2024-01-01") or categories, sorted. These are the positions; keep them as real ISO dates for date axes so the axis spaces correctly. - series: one or more {label, values}; each `values` list lines up 1:1 with x_values (use null for gaps, don't drop entries). - chart_type: "line" (default), "bar", or "area". - units: y-axis units, e.g. "Percent". Axis-label control (both optional — leave unset for normal date axes, which auto-format to the most compact form, e.g. '24 or Jul 24): - x_tick_format: hint the date granularity — "auto" (default), "year", "quarter", "month", or "date". Use e.g. "quarter" to label quarterly points "Q1 '24", or "date" to force full dates. The widget still chooses how many labels fit and spaces them; this only sets the text. - x_labels: fully custom tick text, one per x_value (1:1 with x_values), for labels the widget can't infer — e.g. fiscal quarters ["Q2 FY19", …, "Q2 FY26"] or custom category names. Takes precedence over x_tick_format. x_values still drives positioning; x_labels is only the text. Don't use this just to reformat dates — prefer x_tick_format. Give the chart a finding-style title with the numbers in it (see the chart style guide). Keep it to a handful of series and a few hundred points at most — aggregate in SQL first if needed.

render_chart

Run a read-only SELECT against the FactIQ data warehouse, scoped to one schema — the power tool for joins, pivots and aggregation. Read-only role, 30s statement timeout, with FactIQ's query transforms and series-metadata enrichment. Resolve ids from `series` before filtering `data_points`. `auto_retry=true` opts into a server-side reviser on zero rows. See the `factiq://guide/sql` resource. Returns at most 50 rows. If the query matches more, the result is flagged `"truncated": true` — that is your cue to **aggregate or compute in SQL** (`GROUP BY`, `date_trunc`, a SUM/AVG/rank/ratio) and return that, NOT to try to fetch the raw rows. A chart or a summary needs the aggregated result, and large raw results don't fit in context anyway. `page` is the one exception and works only on the `nasa_fires` schema, where a row is a single fire detection that belongs to no series and so cannot be aggregated into one. Each page holds 50 rows; the answer carries `page` and `has_more`, and you read the next block with `page=2`, `page=3` and so on. Give the query an ORDER BY, otherwise Postgres may return the same row on two pages and never return another. Still prefer a count or a total when that answers the question — paging through thousands of detections does not. Runs through FactIQ's read-only database role, which rejects any attempt to modify data (INSERT/UPDATE/DELETE/DROP/…).

run_sql

Find datasets by keyword across every schema — the first discovery step. Keyword ranking (not semantic) over dataset titles and topics. Returns ranked {schema, dataset_code, title, topic}. Follow up with `describe_dataset` for detail, or `run_sql` on the series/dimensions tables for exact series.

search_datasets

Search earnings-call intelligence from live calls, decomposed into atomic, quote-anchored claims and Q&A pressure points — never a raw transcript dump. Retrieval for "claims" and "pressure_points" is lexical, not semantic. A nonempty query uses an OR-of-tokens full-text candidate set; strict websearch_to_tsquery matches receive a fixed ranking boost, so strict matches rank ahead of loose partial-term matches. Lower-ranked rows may match only some query terms. If that filtered full-text search returns no rows, a trigram fallback handles typos and sub-word variants. Quoted phrases use websearch syntax, but synonyms are not inferred: try the company's own vocabulary ("capex" and "capital expenditure", segment names, abbreviations). An empty or partial result is not proof that management did not discuss a topic; coverage and vocabulary both matter. Target and filter semantics: - "claims" (default) searches or browses normalized management/analyst claim rows. company_filter, quarter_filter, claim_family, section, detail, and limit apply. claim_family matches primary or secondary families; section is "prepared_remarks" or "qa". Base rows include direction/value/unit where checkable, assertion_status, canonical_statement, verbatim_quote, speaker, speaker_role, and section. - "pressure_points" searches or browses what analysts pressed for and whether management confirmed, declined, deflected, or partially answered. company_filter, quarter_filter, claim_family (the linked family), detail, and limit apply. section is ignored because every pressure point is from Q&A. Base rows include specific_ask, response_quality, refused_number, linked_family, linked_claim_id, and verbatim_quote. - "disclosure_profile" is a direct company lookup, not text search. The ticker is the first company_filter value, or query when company_filter is empty. Profiles are curated separately from transcript coverage, so a covered company may not have one; availability is "available" or "unavailable" accordingly. quarter_filter, claim_family, section, detail, and limit do not narrow it. - "coverage" is corpus inventory, not text search. company_filter and limit apply; query, quarter_filter, claim_family, section, and detail do not narrow it. Rows report calls_covered, earliest_period, latest_period, latest_call_date, claims, and pressure_points. An empty query is a capped browse, not a completeness guarantee. Rows are ordered by newest fiscal period/call and then source-block (spoken) order within one call. Every response is limited to at most 50 rows, and a browse that reaches its limit may omit additional rows even if no truncation flag is present. For a latest-call note: (1) call "coverage" with one ticker, (2) read latest_period, (3) call "claims" with that ticker, quarter_filter=latest_period, query="", detail=true, and a deliberate limit, then (4) call "pressure_points" with the same ticker and quarter. Treat both browses as capped evidence, not a complete transcript. Narrow large result sets with ticker, quarter, target, claim_family, and (for claims) section; the restricted `transcripts` schema cannot be queried through run_sql. Evidence rules: canonical_statement is normalized and must not be quoted as verbatim. Quote only verbatim_quote, with its speaker, role, ticker, and fiscal period. assertion_status is load-bearing: analyst_hypothesized is the analyst's framing, while mgmt_declined_to_confirm is a refusal, not a management assertion. Spoken call claims are a different source class from filed XBRL actuals (`run_sql` on the `sec` schema) and formally issued targets (the `sec_guidance` dataset); label them separately. Examples: - Latest call: search_earnings_transcripts(query="", search_target="coverage", company_filter="MU"), then repeat with search_target="claims", quarter_filter=<latest_period>, detail=true, limit=50, and finally with search_target="pressure_points" for the same ticker and quarter. - Cross-company theme: search_earnings_transcripts(query="capital expenditure capacity", search_target="claims", company_filter="MU,NVDA", detail=true); inspect each returned row for topical support, do not assume every term matched, and retry vocabulary as needed. - Disclosure habits: search_earnings_transcripts(query="", search_target="disclosure_profile", company_filter="MU"). claim_family codes: driver_attribution, forward_conviction, prior_view_revision, strategic_agreement, pricing_mechanics, demand_magnitude, capacity_milestone, competitive_positioning, cohort_behavior, cost_margin_bridge, capital_allocation, stated_risk_constraint, qa_dynamics, regulatory_policy, pipeline_backlog, commodity_hedging, ma_deal, labor_org. Topic paths (topic_path column) are rooted at: demand, supply_capacity, pricing, cost_margin, capital, competition, customer, product_roadmap, risk, governance.

search_earnings_transcripts

Deterministically search precomputed intelligence from company executives' podcasts, TV interviews, and conference appearances outside earnings calls. No serving-time model interprets or expands the query. Retrieval for structured findings is lexical, not semantic: strict full-text matching is tried first, loose any-term full-text matching only when strict matching returns no candidates, then trigram matching only when both full-text stages are empty. Natural-language questions work because person names and common question scaffolding are normalized. Prefer concise topical terms; retry synonyms or the speaker's own vocabulary before concluding there is no coverage. Canonical search_target values: - "search" (default): blended structured claims and broad passage cards. - "claims": normalized decision-relevant claims only. - "passages": broad substantive paraphrase cards only. - "pressure_points": stored refusal and declined-to-confirm claim rows. - "appearances": video-level catalog rows. - "coverage": company-level structured-corpus inventory. Compatibility aliases remain accepted for older clients: "all" maps to "search", "videos" to "appearances", and "companies" to "coverage". New callers should use the six canonical values; "search" is the primary blended target, not a legacy alias. Result contracts: - search/claims/passages/pressure_points return result_kind, canonical_paraphrase, speaker, primary ticker, topic labels, video/channel/publication metadata, lexical relevance, and a timestamped YouTube URL. detail=true adds normalized claim and attribution fields but never raw transcript text or evidence spans. - appearances returns video metadata, primary ticker, attribution metadata, matching claim count, URL, and relevance score. detail has no effect. - coverage returns company-level appearance, publication-date-span, channel, claim, and low-confidence-attribution counts. It is structured coverage, not a per-person acquired-video/hour inventory; query, sort, and detail have no effect. Filter and ordering semantics: - company_filter is comma-separated exact primary tickers for structured findings. Catalog targets also match exact stored entity-reference tokens. - person is a case-insensitive substring over the finding speaker and/or appearance speaker metadata. appearance_type applies to every target. - claim_family restricts structured claims. With blended search it suppresses passage cards; pairing it with search_target="passages" is an error. For appearances/coverage it requires videos with matching structured claims. - date_from/date_to are inclusive YYYY-MM-DD bounds on the video's stored publication/upload date, which can differ from the recording or event date. - relevance orders lexical score first, then publication date; newest orders publication date first, then lexical score. Coverage has fixed inventory ordering by latest covered publication date. With an empty query, search and claims browse recent high-signal claims without generic passage cards; passages browses recent passage cards; pressure_points browses recent refusal rows; appearances browses the video catalog; coverage returns the company inventory. Each result set is capped at 1-50 rows. Evidence safety: canonical_paraphrase is a normalized paraphrase, not a transcript quotation. Cite the timestamped YouTube URL with the returned speaker, publication date, and video metadata. Do not put canonical_paraphrase in quotation marks unless you independently verify the wording at the source link. The tool never returns raw transcript text. Detail-field safety: detail adds normalized claim fields only. The extraction pipeline's internal audit flags are not published, because they describe how a row was built rather than anything checkable about the claim; judge a claim from value, assertion_status, and the timestamped source link instead. company_attribution_confidence and company_attribution_source concern only the video-to-company/ticker assignment; inferred attribution is not independent confirmation.

search_media_appearances