Distilla Investment Research

Read one Queryable Entity as a GROUP BY aggregate. Returns one page of groups. Call list_queryable_entities first, then describe_queryable_entities. Allowed entities: company, company_drivers, consensus_data_point, consensus_metric, earnings_calendar, executive_summary, file, financial_data_point, financial_metric, knowledge_unit, ku_cell, price_explanation, product, product_category, sector, standard_event, stock_price, ticker, time_period, valuation_multiple. Public fields: entity, aggregates, group_by, filters, filter_logic, having, sort, joins, limit. Functions: COUNT, SUM, AVG, MIN, MAX. filters = WHERE; having = HAVING. Pass IN lists on filter.value. Do not pass value_path. A full page sets truncated=true. This call does not scan the full universe.

aggregate_entity

Describe the schema of one or more Queryable Entities. Call list_queryable_entities first. Call this before query_entity and aggregate_entity. Pass a comma-separated list of names (for example company,file). Returns field names, types, descriptions, operators, and select, filter, and sort flags.

describe_queryable_entities

Read one Public Library document by document_id from search_public_library sources. Returns metadata, summary, and tags. Pass with_contents=true to include raw text in contents for non-Research documents. Research documents omit full text (provider terms) and set contents_omitted. Omit with_contents to keep the payload small. This tool does not search. Call search_public_library first.

get_library_document

Read one screen job that you started with screen_drivers or screen_earnings. Pass the job_id from the start call. This tool only reads GCS; it does not run the screen. Poll until status is "done" or "error". When status is "done", the payload includes result with the same JSON shape as a finished screen: summary, matches, ranked, and coverage. When status is queued or running, result is absent. When status is error, the payload includes error.

get_screen_job

List Queryable Entities this MCP may read. Call this before describe_queryable_entities, query_entity, and aggregate_entity. Returns names, descriptions, aliases, and join_targets.

list_queryable_entities

Return pong plus the input message.

ping

Read one Queryable Entity. Returns one page of rows. Call list_queryable_entities first, then describe_queryable_entities. Allowed entities: company, company_drivers, consensus_data_point, consensus_metric, earnings_calendar, executive_summary, file, financial_data_point, financial_metric, knowledge_unit, ku_cell, price_explanation, product, product_category, sector, standard_event, stock_price, ticker, time_period, valuation_multiple. Public fields: entity, select, filters, filter_logic, sort, joins, limit. Pass IN lists on filter.value. Do not pass value_path. A full page sets truncated=true and may include next_hint. For GROUP BY aggregates (COUNT, SUM, AVG, MIN, MAX), use aggregate_entity.

query_entity

Screen each company's fundamental drivers profile against a qualitative criterion such as "depends on AI hyperscaler capex as a primary growth driver", "sensitive to long-end interest rates via mortgage origination", or "tied to Chinese consumer recovery via outbound tourism". Backed by company_drivers — one synthesized profile per company (Type / Topic / Causal Relationship / Support / Current vs. Past). This is durable thesis-level structure, not a recent event. Pass exactly ONE of: - company_ids: explicit DB integer IDs (obtain via query_entity on company). - universe: region scopes from all, us, cn, jp, hk, kr. Use ["all"] to scan every company; combine codes (e.g. ["us", "jp"]) to scan those HQ regions. If both are set, company_ids wins and universe is ignored. No per-call upper bound on N. Large universes including universe=["all"] run in 100-company chunks. No lookback_days — drivers are continuously updated, one row per company. This tool starts a screen job and returns at once. It does not run the screen in this request. The first response is { "job_id": "...", "status": "queued" }. Then call get_screen_job(job_id) until status is "done" or "error". When status is "done", get_screen_job returns result with summary, matches (the top_n rows), ranked (the full ranked list), and coverage counts. There is no VFS path. Use this tool for what drives a company over time. Do not use it for a discrete dated event, earnings-period commentary, or a price move attributed to a driver. For exact field filters on company_drivers, use query_entity. Do not split one thematic question into parallel sector-narrowed calls. Issue one call at the full universe scope, raise top_n if you need more breadth, and group sectors from ranked.

screen_drivers

Screen each company's earnings-document cells (10-K, 10-Q, 8-K, 20-F, 6-K, earnings-call transcripts, and related filings extracted into the Earnings Database) against a qualitative criterion such as "explicitly mentioned tariff exposure as a near-term margin headwind" or "called out AI-driven cost savings as a guidance lever". Backed by ku_cell — LLM-extracted structured content from each company's most recent composite earnings filings per fiscal reporting period. Pass exactly ONE of: - company_ids: explicit DB integer IDs (obtain via query_entity on company). - universe: region scopes from all, us, cn, jp, hk, kr. Use ["all"] to scan every company; combine codes (e.g. ["us", "jp"]) to scan those HQ regions. If both are set, company_ids wins and universe is ignored. No per-call upper bound on N. Large universes including universe=["all"] run in 100-company chunks. periods is the number of recent reporting periods per company (default 4). This tool starts a screen job and returns at once. It does not run the screen in this request. The first response is { "job_id": "...", "status": "queued" }. Then call get_screen_job(job_id) until status is "done" or "error". When status is "done", get_screen_job returns result with summary, matches (the top_n rows), ranked (the full ranked list), and coverage counts. There is no VFS path. Use this tool for qualitative earnings-period commentary. Do not use it for durable thesis-level drivers (use screen_drivers) or for exact field filters on ku_cell (use query_entity). Do not split one thematic question into parallel sector-narrowed calls. Issue one call at the full universe scope, raise top_n if you need more breadth, and group sectors from ranked.

screen_earnings

Search the Public Library. The corpus is the public library, not the caller's private uploads. This is the same Agent2 public-library search: an LLM scope planner plus semantic retrieval. query is required and is a natural-language question or topic (for example "Acquired NVDA episode" or "tariff exposure"). Optional doc_types restrict Type tags: "Research", "Podcast". Optional tickers scopes to Company tags. Optional date_range is one lookback bucket: 7d, 30d, 60d, 90d, 180d, 200d, 1y. Optional brokers hard-filters by research firm. sole_company=true keeps only single-Company-tag docs and needs exactly one ticker. mode is synthesize (default) or list. synthesize returns a narrative answer from semantic passages (a sample, not an exhaustive sweep). list returns a catalog of matching documents. Unknown mode values become synthesize, as in Agent2. The JSON has mode, answer, and sources. Each source has document_id plus title, type, and publication_date when present. This tool does not return full document text. Call get_library_document with one document_id for tags or raw text. Research documents never include full text.

search_public_library