"How to perform SELECT queries in PostgreSQL" matching MCP tools:

• researchA

  SofyaA

  Perform comprehensive research on a topic. Decomposes your query into sub-queries, searches and reads multiple sources in parallel, then synthesizes a structured report with citations. Best for open-ended or comparative questions that need coverage from many angles. For simple factual lookups, use search instead (optionally with include_answer=true for cheap synthesis). Costs 50 credits. Returns: query, report (structured markdown with citations), sources (array of {title, url, fetched}), sub_queries (the decomposed queries), credits_used, credits_remaining, usage (token counts). Args: query: The research question or topic topic: "general" (default) or "news" (prioritize recent news articles) freshness: Filter by recency - "day", "week", "month", "year", or "YYYY-MM-DD:YYYY-MM-DD" max_sources: Maximum number of sources to use, 5-30 (default 20)

  ConnectorNo auth

• fema_dataframe_queryA

  fema-mcp-serverA

  Run a read-only SQL SELECT against a DataCanvas table staged by fema_search_nfip. Enables aggregation, GROUP BY, SUM/COUNT, time-series, and filtered analysis over the full NFIP claims result without re-fetching from the API. Call fema_dataframe_describe first to get the exact table name and column names needed for valid SQL. Only SELECT statements are allowed — DDL, DML, COPY, and file-reading functions are blocked. A response holds at most 100,000 characters of rows; when it holds fewer rows than the query returned, truncated and shown are set and the notice says how to page with OFFSET.

  ConnectorNo auth

• create_guidesA

  serpmanticsA

  Create new guides Create one or more new guides based on provided queries. Each guide targets exactly ONE engine and ONE analysis mode, chosen with the optional `source` field (default `google`). How to request each guide type: 1. Google SERP guide (1 credit per guide): omit `source`, or pass `source: "google"`. Example payload: {"queries": ["best crm"], "lang": "en-us"} 1bis. Google AI Overview guide (1 credit per guide). Two modes, like AI engines: `source: "google_ai_overview"` builds the guide from the TEXT of Google's AI answers (AI Overview, completed with AI Mode answers) ; `source: "google_ai_overview_citations"` builds it from the content of the web SOURCES those answers cite (recommended for GEO). Same language/country parameters as a Google SERP guide, 1 credit per guide in both modes. Example payload: {"queries": ["best crm"], "lang": "en-us", "source": "google_ai_overview_citations"} 2. LLM ANSWER guide (4 credits per guide): pass the engine name alone, e.g. `source: "chatgpt"`. The guide is built from the answer text the AI generates for the query. Example payload: {"queries": ["best crm"], "lang": "en-us", "source": "chatgpt"} 3. LLM CITATIONS guide (4 credits per guide) [RECOMMENDED AI mode]: pass the engine name with the `_citations` suffix, e.g. `source: "chatgpt_citations"`. The guide is built from the content of the web pages the AI cites in its answer. Example payload: {"queries": ["best crm"], "lang": "en-us", "source": "chatgpt_citations"} Which AI mode to pick? For GEO (getting a page visible in AI answers), prefer `<engine>_citations`: AI engines send traffic by CITING pages as sources, so the winning move is to look like the pages they cite. The answer-text mode (`<engine>` alone) is mostly useful to analyze how the AI phrases its own answer. When in doubt, pick `<engine>_citations`. The same two modes exist for every AI engine (chatgpt, perplexity, claude, gemini, grok, mistral, deepseek). To optimize the same page for several engines or modes (e.g. Google AND ChatGPT answers AND ChatGPT sources), create one guide per source value on the same query. IMPORTANT, HOW TO READ THE RESPONSE OF THIS ENDPOINT, WHICH SPENDS CREDITS. Queries listed in `guidesFailed` are PROVEN not to have produced a guide and their credit was given back (unless the account has unlimited credits, where nothing was reserved): re-sending them is free and correct. Queries listed in `guidesUnknown` have an UNDECIDABLE outcome and their credit is deliberately KEPT, because the guide was most likely written: DO NOT re-send them, you would pay for the same guide twice. Look them up in `GET /api/v1/guides` after a few minutes instead, and contact support if nothing shows up. Finally, a `200` is NOT a promise that every query produced a guide: compare `guides.length` with the number of queries you sent, never read `success` alone, and never re-send a query just because it is missing from `guides`.

  ConnectorNo auth

• query_databaseA

  flootA

  Run a READ-ONLY SQL query against the project's Postgres database (SELECT, EXPLAIN, etc.) — the Floot-managed database, or an external Postgres the user connected to the project. Writes are rejected — use execute_sql for those. Returns JSON: `{rows, rowCount, command, truncated?}` (or `{results: [...]}` for multi-statement queries). Pass `database` only if the project has more than one.

  ConnectorOAuth

• query_databaseA

  HostingFor.AIA

  PHP + MySQL plans: runs one read-only SQL statement (SELECT, SHOW, DESCRIBE, EXPLAIN or WITH … SELECT) against the site's database and returns the rows. Runs in a read-only transaction; use execute_sql to change data. Use ? placeholders with params for values.

  ConnectorOAuth

• start_auto_clusteringA

  MencoroA

  Start a background job that proposes how to group up to 500 tracked queries into clusters. mode "fill_gaps" places only queries that have no cluster yet; "add_on_top" adds clusters without touching existing memberships; "full_regroup" proposes a fresh grouping of every query. restrictToExistingClusters only uses the project's current clusters. Nothing changes until apply_auto_clustering: poll get_job, show the proposal to the user, then apply it. Requires an active subscription.

  ConnectorOAuth

• sixta_analyze_workloadA

  sixta-connectA

  Find your worst queries by TOTAL time — no connection needed. Paste a MySQL slow query log or a PostgreSQL pg_stat_statements export and get a ranked top-N: each query shape with calls, total/mean time, and (slow log) the rows-examined-to-sent ratio, fingerprinted so thousands of log lines collapse into a few classes. Flags the dominant query, N+1 patterns, and full-scan ratios, reports how concentrated the load is (what share of total time the top shapes own), and hands the worst offenders to sixta_analyze_query. Call this whenever the user shares a slow query log or pg_stat_statements export — even a long one — or asks which queries are slowest: summing time across thousands of log lines is arithmetic a model cannot do reliably by eye. Input is analyzed in memory and never stored.

  ConnectorOAuth

• execute_sqlA

  Google Big QueryA

  Run a SQL query in the project and return the result. Prefer the `execute_sql_readonly` tool if possible. This tool can execute any query that bigquery supports including: * SQL Queries (`SELECT`, `INSERT`, `UPDATE`, `DELETE`, `CREATE`, etc.) * AI/ML functions like `AI.FORECAST`, `AI.KEY_DRIVERS`, `ML.EVALUATE`, `ML.PREDICT` * Any other query that bigquery supports. Example Queries: ```sql -- Insert data into a table. INSERT INTO `my_project.my_dataset`.my_table (name, age) VALUES ('Alice', 30); -- Create a table. CREATE TABLE `my_project.my_dataset`.my_table ( name STRING, age INT64); -- DELETE data from a table. DELETE FROM `my_project.my_dataset`.my_table WHERE name = 'Alice'; -- Create Dataset CREATE SCHEMA `my_project.my_dataset` OPTIONS (location = 'US'); -- Drop table DROP TABLE `my_project.my_dataset`.my_table; -- Drop dataset DROP SCHEMA `my_project.my_dataset`; -- Create Model CREATE OR REPLACE MODEL `my_project.my_dataset.my_model` OPTIONS ( model_type = 'LINEAR_REG' LS_INIT_LEARN_RATE=0.15, L1_REG=1, MAX_ITERATIONS=5, DATA_SPLIT_METHOD='SEQ', DATA_SPLIT_EVAL_FRACTION=0.3, DATA_SPLIT_COL='timestamp') AS SELECT col1, col2, timestamp, label FROM `my_project.my_dataset.my_table`; ``` Queries executed using the `execute_sql` tool will always have the default job label `goog-mcp-server: true` automatically set in addition to any custom `labels` provided in the request. Queries are charged to the project specified in the `project_id` field. Query Execution Behavior: * If the query completes within the synchronous timeout (default 20 seconds or custom `timeout_ms`), the tool returns `job_complete: true` and the initial result rows directly. For fast queries, `job_id` may be omitted as no persistent background job is created; no further action or polling is needed. * If the query takes longer than `timeout_ms`, the tool returns `job_complete: false` and a `job_id`. In this case, use the `get_query_results` tool with `job_id` to poll until `job_complete: true`, or use `cancel_job` to abort the running query. * You can optionally specify `timeout_ms` to configure the maximum synchronous wait time in milliseconds (defaults to 20,000 ms), and `job_timeout_ms` to enforce a hard server-side timeout after which BigQuery automatically terminates the job.

  Connector

  Destructive

  No auth

• arena_get_strategy_performanceA

  Backtesting ArenaA

  How did this exact strategy, asset and interval perform? Aggregated backtest performance for ONE specific (strategy, asset, interval) combination. Returns run_count, avg_cagr, avg_win_rate, avg_drawdown, effective_years, vs_buy_hold comparison (beats_buy_hold, cagr_delta) and an `evidence` block declaring the gate machine-readably (gate_applies_to: stats.run_count, threshold 5 runs, benchmark value, aggregation data window). For multi-strategy overview use arena_get_strategy_insights. Use this to answer 'How does strategy X perform on asset Y?'. [Free tier]

  ConnectorNo auth

• generate_queryA

  ALM X++ MCP ServerA

  WHEN: developer needs correct X++ select or T-SQL for D365 tables with proper joins. Triggers: 'X++ select', 'generate a query', 'SQL for', 'join with', 'how to query', 'générer une requête', 'write a select statement', 'select from', 'X++ query for', 'requête X++', 'écrire une select'. Generate both X++ select statements and equivalent T-SQL queries for D365 F&O tables. Uses real field names, relations, and indexes from the knowledge base to produce correct joins. Supports: field selection, multi-table joins (auto-detects relations), WHERE filters, ORDER BY, TOP/firstonly, cross-company. Also accepts natural language descriptions like 'find all open sales orders for customer 1001 with CustTable join'. [!] For multi-table joins, call find_related_objects (or get_relation_graph if the relation index is loaded) FIRST to get the correct FK relations -- this tool will then produce accurate join conditions. [!] The generated X++ is a template -- adapt it to your custom code context before using in production. Returns side-by-side X++ and SQL with explanations.

  ConnectorNo auth

• query_dbA

  FinBridgeA

  Run a single read-only SELECT query against the local finbridge database (ingested KR/US fundamentals, filings, daily prices). The statement must start with SELECT or WITH; multiple statements, PRAGMA, and any write/DDL keywords (INSERT/UPDATE/DELETE/DROP/ALTER/CREATE/ATTACH/...) are rejected. The query runs in a separate read-only process with SQLite authorization, a 2-second deadline, two concurrent queries per server process, and a 1 MB result budget. Free accounts cannot query raw history or history views; the latest-annual snapshot remains available. The escape hatch for questions no dedicated tool answers — Japan, Taiwan and Europe are largely reachable only this way. Prefer get_valuation or get_peers for ordinary single-company work (they handles per-market period and currency rules that a hand-written query will get wrong), and call get_db_schema first for the table shapes. Args: - sql: one SELECT (or WITH ... SELECT) statement. A single trailing ';' is tolerated. - limit: max rows returned, 1-500 (default 50) - response_format: 'markdown' (default, table) or 'json' (compact) Returns: {columns: [name], rows: [[cell, ...]], row_count, truncated} — truncated=true means more rows matched than 'limit'. Examples (v_financials / v_latest_annual views are the easiest entry points): - Largest companies by latest annual revenue: "SELECT name, ticker, stock_code, fiscal_year, revenue FROM v_latest_annual ORDER BY revenue DESC LIMIT 10" - Samsung Electronics annual trend: "SELECT fiscal_year, revenue, operating_income, net_income FROM v_financials WHERE stock_code = '005930' AND quarter = 0 ORDER BY fiscal_year DESC" - KR vs US company counts: "SELECT source, COUNT(*) AS n FROM companies GROUP BY source" - Recent Samsung Electronics closes: "SELECT date, close FROM prices_daily p JOIN companies c ON c.id = p.company_id WHERE c.stock_code = '005930' ORDER BY date DESC LIMIT 20" (prices_daily holds KR, US, TW; US history starts 2023-03-28) Use when: custom aggregation/joins over ingested data that no dedicated tool expresses. Don't use for anything that writes — it will be rejected. FinBridge has no real-time equity quote tool — equity prices here are end-of-day closes from the nightly ingest; the only live data is regulator filings (get_dart_filings / get_edgar_filings). Errors: non-SELECT input, ';' inside, or forbidden keywords -> rejected with the reason; unknown table/column -> SQL error with a hint to call get_db_schema first.

  ConnectorAPI key

• analyze_citation_structure_batchA

  VantageA

  Analyze the structural shape of the winning AI answer across several related keywords/topics in one call: does each lead with a list, how long is the opening, how many sources it cites. It covers a topic cluster in one call, for content planning across related pieces. Read-only: no side effects, safe to retry. Costs 1 quota unit per keyword in the batch (free tier is 30 units/month shared across all the metered tools, so up to 30 keywords total that period if nothing else is used). A per-keyword provider error doesn't fail the whole batch - that keyword's entry just carries an "error" field instead. Returns: {"results" (list, one {"keyword", ...same shape as analyze_citation_structure, or "error"} per keyword, in the order given), "summary": {"topics_analyzed", "topics_requested", "list_led_count", "avg_sources_cited", "avg_community_pct" (average source_mix.community_pct across the analyzed topics)}}. Args: keywords: topics/queries to analyze, e.g. ["how to reduce churn", "churn rate benchmarks", "reduce customer churn saas"]. Max 10. country: market to read the answers in, e.g. "Italy". Defaults to "United States". language: language code, e.g. "it". Defaults to "en". engine: "chat_gpt" (default), "gemini" or "perplexity", as in analyze_citation_structure. One answer per topic.

  ConnectorAPI key

• get_yandex_query_statsA

  SEO GSC Wizard MCPA

  Use this when the user asks which searches bring their site visitors from Yandex: Yandex Webmaster queries for a property, with impressions, clicks, average impression position and average clicked position. IMPORTANT COVERAGE LIMIT: Yandex exposes only its top queries for the period (at most 3,000), so totals from these rows are a floor, not a complete figure, and any share is of the listed queries, not of the site; say so when reporting them. Yandex serves 500 rows per request and this tool pages through as many as `limit` allows, defaulting to the first 500 by `orderBy`, sorted by that metric, highest first; `totalAvailable` and `truncated` say how much of the pool was left. CTR is a percentage (0-100, like every other CTR this server returns), calculated from impressions and clicks because Yandex publishes no CTR metric; null when there were no impressions. Yandex has no query-by-page report, so these queries cannot be attributed to URLs. Not for Google or Bing queries: use query_top_queries or get_bing_query_stats. The dates select a real window, so a longer range returns more queries and larger totals; omitting them gives the last week only. Yandex finishes a day about four days late, so `range` echoes the window it actually served. The full set of caveats is in `limitations` on every response. Returns notConfigured: true (with a `reason`) when no Yandex account is connected, the connection has to be granted again, the property is not in it, or the caller holds a share of the property rather than owning it: Yandex data is owner-only.

  ConnectorOAuth

• analyze_query_shapesA

  SEO GSC Wizard MCPA

  Use this when the user asks whether AI Overviews / AI Mode fan-out shows in a property's queries: classifies them by SHAPE (the app's Query Analysis report). Not for change over time: use get_query_shape_trend. Buckets: reply artefacts ("yes", "show me"), pivot follow-ups ("what about gemini"), conversational questions, LLM tracker probes, agent harnesses, pasted strings, long uncategorised, and conventional keywords. Returns per-bucket query/click/impression counts and shares (0-100), zero-click counts, average position, and top example queries (ctr 0-100), plus an `aiShaped` roll-up of the conversational buckets. Shares are of visible queries only: `coverage.hiddenClicksShare` (0-1) is the share of the property's clicks with no query row. Buckets run in `bucketOrder` (conventional last), examples by impressions, highest first. Heuristic pattern matching in 27 languages: it judges how a query LOOKS, not what the searcher meant, so treat single rows as evidence rather than proof. Reads the ClickHouse warehouse when available, else the live Search Console API. Date range is optional: omit startDate/endDate to analyze the last 28 settled days (data lags ~2-3 days).

  ConnectorOAuth

• billing_budgetsA

  GreatWaitB

  Replaces the caller organization's advisory execution budget and optional hard execution-spend cap for the current UTC month. Values are exact whole cents encoded as integer microdollars. The expected budget-policy revision prevents a stale editor overwriting a newer choice, and Idempotency-Key makes retrying one intent safe. Null turns that setting off. This command records desired state durably; billing.reconcile advances it to PostgreSQL, and reads remain pending until PostgreSQL confirms it. It never changes automatic top-up authority, purchases credit, or counts subscription payments as execution usage.

  ConnectorOAuth

• execute_sql_readonlyA

  Google Big QueryA

  Run a read-only SQL query in the project and return the result. Prefer this tool over `execute_sql` if possible. This tool is restricted to only `SELECT` statements. `INSERT`, `UPDATE`, and `DELETE` statements and stored procedures aren't allowed. If the query doesn't include a `SELECT` statement, an error is returned. For information on creating queries, see the [GoogleSQL documentation](https://cloud.google.com/bigquery/docs/reference/standard-sql/query-syntax). IMPORTANT: For predictive and analytical tasks (forecasting, anomaly detection, key driver / root cause analysis, classification, churn prediction, or text generation), ALWAYS execute computation in-warehouse using BigQuery native AI/ML functions (`AI.FORECAST`, `AI.DETECT_ANOMALIES`, `AI.KEY_DRIVERS`, `AI.CLASSIFY`, `AI.GENERATE`) rather than exporting raw rows to a local Python sandbox. In-warehouse execution scales to billions of rows, preserves governance, and eliminates data egress latency. Example Queries: ```sql -- Count the number of penguins in each island. SELECT island, COUNT(*) AS population FROM bigquery-public-data.ml_datasets.penguins GROUP BY island -- Forecast data using AI.FORECAST SELECT * FROM AI.FORECAST(TABLE `project.dataset.my_table`, data_col => 'num_trips', timestamp_col => 'date', id_cols => ['usertype'], horizon => 30) -- Detect anomalies in time series data using AI.DETECT_ANOMALIES SELECT * FROM AI.DETECT_ANOMALIES( TABLE `project.dataset.historical_metrics`, TABLE `project.dataset.recent_metrics`, data_col => 'num_requests', timestamp_col => 'timestamp' ) -- Identify key drivers of metric changes using AI.KEY_DRIVERS SELECT * FROM AI.KEY_DRIVERS( TABLE `project.dataset.sales_summary`, metric_col => 'total_revenue', dimension_cols => ['region', 'product_category'], interest_label_col => 'is_current_quarter' ) -- Classify text into categories using AI.CLASSIFY SELECT ticket_id, AI.CLASSIFY(ticket_text, ['Billing', 'Technical Support', 'Feature Request']) AS category FROM `project.dataset.support_tickets` -- Generate text or summaries using AI.GENERATE SELECT review_id, AI.GENERATE(CONCAT('Summarize this customer review: ', review_text)).result AS summary FROM `project.dataset.reviews` ``` Queries executed using the `execute_sql_readonly` tool will always have the job label `goog-mcp-server: true` automatically set in addition to any custom `labels` provided in the request. Queries are charged to the project specified in the `project_id` field. Query Execution Behavior: * If the query completes within the synchronous timeout (default 20 seconds or custom `timeout_ms`), the tool returns `job_complete: true` and the result rows directly. For fast queries, `job_id` may be omitted as no persistent background job is created; no further action or polling is needed. * If the query takes longer than `timeout_ms`, the tool returns `job_complete: false` and a `job_id`. In this case, use the `get_query_results` tool with `job_id` to poll until `job_complete: true`, or use `cancel_job` to abort the running query. * You can optionally specify `timeout_ms` to configure the maximum synchronous wait time in milliseconds (defaults to 20,000 ms), and `job_timeout_ms` to enforce a hard server-side timeout after which BigQuery automatically terminates the job.

  ConnectorNo auth

• batch_searchA

  AnySearchA

  This is Anysearch's parallel search tool. Parallel search — run multiple Anysearch queries in a single call. Prefer this over multiple sequential calls when you have 2–5 queries. Saves context space and returns all results at once. Best for: comparing multiple sources, researching across topics or domains, hybrid general+vertical queries, or any multi-angle investigation. ## When to use Use batch_search instead of multiple sequential search calls when you have 2–5 independent queries. 🏆 PRIMARY use case: After get_sub_domains(domains=[...]) returns sub_domains across multiple domains, use batch_search to send one query per sub_domain in parallel. This is more efficient than sequential per-domain search calls. Also useful for ambiguous / fuzzy queries within a single domain: after get_sub_domains, use batch_search to explore multiple sub_domains in parallel. ## Constraints - Maximum 5 queries per call - Each query item follows the search tool parameter structure (query is required; domain, sub_domain, sub_domain_params are optional. For general queries, omit all domain fields. For vertical queries, domain + sub_domain + sub_domain_params MUST come from get_sub_domains(domain=<domain>) output — same rules as the search tool) - Queries run in parallel; a single query failure does not block others - REQUIRED PARAMS: Same rule as search — when a required param from get_sub_domains is not applicable, pass it as an empty string (key: ""). Never skip required params. ## Examples ### Single-domain batch (multiple sub_domains) Instead of: search(query="latest TSLA earnings", domain="finance", sub_domain="finance.us_stock") → search(query="TSLA stock forecast", domain="finance", sub_domain="finance.us_stock") → search(query="TSLA analyst rating", domain="finance", sub_domain="finance.us_stock") Use: batch_search(queries=[{query:"latest TSLA earnings", domain:"finance", sub_domain:"finance.us_stock"}, {query:"TSLA stock forecast", domain:"finance", sub_domain:"finance.us_stock"}, {query:"TSLA analyst rating", domain:"finance", sub_domain:"finance.us_stock"}]) ### Multi-domain batch (after get_sub_domains with multiple domains) After: get_sub_domains(domains=["finance", "health", "legal"]) Use: batch_search(queries=[ {query:"AI regulation impact on healthcare stocks 2025", domain:"finance", sub_domain:"finance.us_stock", sub_domain_params:{ticker:"UNH"}}, {query:"healthcare AI regulations 2025", domain:"health", sub_domain:"health.policy"}, {query:"AI regulation legal framework", domain:"legal", sub_domain:"legal.legislation"}]) ### Hybrid: general + vertical in parallel (universal pattern for any borderline query) Use this whenever you are unsure if the query is pure encyclopedia or domain-specific — fire BOTH channels in batch_search: batch_search(queries=[ {query:"..."}, // general — no domain {query:"...", domain:"...", sub_domain:"..."}]) // vertical channel(s) This applies universally: classical texts, financial concepts, legal theories, historical events, scientific discoveries, medical topics — any query where domain knowledge could enrich the encyclopedia answer.

  Connector

  Destructive

  No auth

• search_docsA

  pg-aiguideA

  Search documentation with hybrid semantic (vector) and keyword (BM25) search. Use semanticWeight to choose keyword-only (0), semantic-only (1), or a blend; mid values fuse rankings with RRF. Supports Tiger Cloud (TimescaleDB), PostgreSQL, and PostGIS.

  ConnectorNo auth

• get_buying_guideA

  cyclesite-mcp-serverA

  Search Cyclesite's expert buying guides (24+ articles by cycling-journalism authors). Returns up to 3 matching guides with title, excerpt, difficulty, reading time, and URL. Use for educational queries that don't need live inventory. Example: 'how do I choose a bike size?', 'tips for buying a used e-bike'.

  ConnectorNo auth

• run_sqlA

  Drillr — The financial MCP for AI agentsA

  PostgreSQL SELECT over financial / market / alt-data tables — returns structured rows. Hard rules (query fails otherwise): - SELECT only, no CTE (`WITH ... AS`) — use subqueries. - Period columns are TEXT, not dates — `period_end` is 'YYYY-MM'. Compare as strings (`period_end >= '2024-01'`); a `::date` cast on it fails. - Filter structured tables by ticker (`WHERE ticker IN ('AAPL','MSFT')`; screening: add `ticker NOT LIKE '%-%'` to drop preferred stock). Tables by domain (get_table_schema gives columns + coverage note): - Market: price_volume_history (OHLCV history; MUST filter ticker + time_frame), index_price, equity_extended_rt (pre/after/overnight quotes) - Fundamentals: financial_statements (GAAP income/balance/cashflow), company_snapshot (ratios, per-share, growth) - Earnings: earning_call_summary, earning_call_calendar - Analyst: analyst_ratings, analyst_ratings_consensus - Ownership: insider_and_institution_activities - 8-K events: executive_change, company_deal_events, debt_issuance, securities_offering - Executives: executive_profile, executive_compensation - Alt-data: macro / industry / trade / AI-supply-chain — call list_tables(categories=[...])

  ConnectorNo auth

• precis_orientationA

  Précis Finance MCPB

  Call this first. Returns how to use Précis over this connector: the data model (scenarios, metrics, statements, dimensions), the reporting-tool variants, and how to build charts. Read it before composing queries.

  ConnectorNo auth