Distilla

Read one Queryable Entity as a GROUP BY aggregate. Returns one page of groups. Use this for a count, a per-group min/max/avg, or a threshold screen. Do not page raw rows through query_entity for those. Call list_queryable_entities first, then describe_queryable_entities.

Use cases:

• Count rows or companies that match filters: COUNT on id, with the same filters as a row query.

• Range over time: MIN and MAX of a metric, group_by company_id. Example: forward P/E on valuation_multiple, filter type and valuation_date, alias the outputs minPe and maxPe.

• Threshold screen: MAX(value) as an alias, category and date filters in filters, numeric cut in having on that alias. The number of returned groups is the match count. Then query_entity on company with id in [...] to attach names, tickers, and hq_country.

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, website_change.

Arguments: entity, aggregates, group_by, filters, filter_logic (AND or OR, default AND), having, sort, joins, limit. Each aggregate has function, field, optional alias, and optional distinct. Functions: COUNT, SUM, AVG, MIN, MAX. AVG is the arithmetic mean, not a median or percentile. filters are WHERE. having is HAVING and may use aggregate aliases. sort may use those aliases. Joins and camelCase aliases work like query_entity. Pass IN lists on filter.value. Do not pass value_path.

valuation_multiple.value, financial_data_point.value, and consensus_data_point.value are text. MIN, MAX, SUM, and AVG cast them to numeric and skip rows that are not decimals. A WHERE filter on those fields stays lexicographic. Put the numeric threshold in having, not in filters.

On financial_data_point, join time_period (alias timePeriod) and financial_metric (alias financialMetric). Group by timePeriod.company_id. Do not join company. Put metric name, provenance, duration, and fiscal_year in filters.

A full page sets truncated=true. This call does not scan the full universe. There is no next_hint. If truncated, tighten filters, drop a group_by column, or split an id list into smaller IN batches that each stay under max_limit. limit is capped at max_limit.

Describe the schema of one or more Queryable Entities. Call list_queryable_entities first. Call this before query_entity and aggregate_entity. Pass one comma-separated list of the base entity and every join target you will use (for example financial_data_point,time_period,financial_metric). Prefer suggested_describe_entities from the catalog. Returns field names, types, descriptions, operators, select/filter/sort flags, join_target, join_relation when it differs from join_target, max_limit, and notes. An unknown name returns known_entities. Use only fields and operators from this response.

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. For a YouTube document that is the full video transcript, and the payload also has the video url and channel_name. 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.

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.

List Queryable Entities this MCP may read. Call this before describe_queryable_entities, query_entity, and aggregate_entity. The catalog is the source of truth: names, descriptions, aliases, one-hop join_targets, and suggested_describe_entities. Pass suggested_describe_entities into one describe_queryable_entities call. Coverage is the public catalog: companies, prices, fundamentals, valuation, consensus, events, filings, and filings-derived knowledge-unit cells. There is no private watchlist, portfolio, or user profile.

Return pong plus the input message.

Read one page of rows from one Queryable Entity. Use this for a point lookup, a multi-period history, or a filtered list. Call list_queryable_entities first, then describe_queryable_entities.

What you can read:

• company: name, ticker, sector, HQ country. Screen tools need these integer ids.

• stock_price: daily prices, volume, dividends, and splits. price_explanation: why a significant price move happened.

• financial_data_point: one row per statement line or ratio per fiscal period. Join time_period and financial_metric. Discover metric keys on financial_metric (name ilike '%net_debt%' finds pre-computed ratios).

• valuation_multiple: multiples such as P/E and EV/EBITDA, by company and date.

• consensus_data_point: analyst estimates. Join time_period and consensus_metric. The fiscal period is on time_period (fiscal_year, fiscal_quarter, end_date). consensus_date is the estimate snapshot date, not the fiscal period.

• earnings_calendar: earnings dates. standard_event: corporate events; for earnings, filter type to "Earnings announcement". file: filings, transcripts, and news. Join company and filter company.symbol.

• ku_cell: filings-derived knowledge-unit cells. Filter ku.name. An unfiltered read is a short slice of ids. knowledge_unit holds the definitions. company_drivers and executive_summary are per-company narratives. For a qualitative screen across many companies, use screen_drivers or screen_earnings.

• sector, product, product_category (companies that share a category are peers), and ticker: catalogs. List sector in full. Do not resolve a sector with a single-word ilike filter.

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, website_change.

Arguments: entity, select, filters, filter_logic (AND or OR, default AND), sort, joins, limit. Filter operators: =, !=, >, >=, <, <=, in, ilike, between, isNull, isNotNull. ilike is a case-insensitive pattern match. Use it to search text such as company names, metric names, and valuation types (stored as NTM_Pe_Med_W). Text search uses ilike with '%x%'. Do not use contains on text fields. For date ranges, use between with [start, end] or >= and <=. Do not use like or ilike on dates. There is no "not in". Use one != filter per value with filter_logic AND. For null checks, use isNull or isNotNull and omit value. Pass IN lists on filter.value. in takes one list item per value, for example ["AAPL","MSFT"]. Do not pass one comma-joined string such as ["AAPL,MSFT"]. Do not pass value_path.

Joins: when a field has join_target, join that table and use alias.field in select, filters, and sort. Many tickers in one query: company.symbol in ["AAPL","MSFT"]. Use a camelCase alias (timePeriod, financialMetric). joins.relation is join_relation when describe shows it, otherwise join_target. On ku_cell, describe knowledge_unit but join relation ku. Other ku_cell relations: groupCompany, cellTimePeriod, cellStandardEvent, groupProductCategory, cellProductCategory. The name product_category matches two ku_cell relations, so it is rejected.

financial_data_point accepts only time_period and financial_metric joins. Do not join company. Filter timePeriod.company_id. Reported actuals use timePeriod.provenance = financials and timePeriod.duration = year. This table has no geographic revenue, segment mix, customer geography, or tariff exposure.

valuation_multiple.value, financial_data_point.value, and consensus_data_point.value are stored as text. A filter on them compares lexicographically ("100" < "99"). For a numeric threshold, use aggregate_entity and put the cut in having.

company.hq_country stores codes and full names. Filter both, for example in ["JP","Japan"]. HK is Hong Kong. CN is mainland China. They do not overlap. Other codes: US, KR.

limit is capped at the entity max_limit (from describe); a larger value returns one capped page with limit_note. A full page sets truncated=true and may include next_hint {field:"id", op:">", value:}. Add that filter, sort by id asc, include id in select, and repeat. There is no offset. For COUNT, SUM, AVG, MIN, MAX, or a threshold screen, use aggregate_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 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.

Search the Public Library. The corpus is the public library, not the caller's private uploads. It holds sell-side research summaries, investor podcast episodes, YouTube video transcripts from finance channels, and industry newsletters. 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", "YouTube", "Newsletter". Pass ["YouTube"] when the user asks for videos, a YouTube channel, or what was said on camera. 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.