"DuckDB" matching MCP tools:

• bls_dataframe_query

  bls-labor-mcp-server

  Run a single-statement SELECT against the canvas dataframes registered by bls_get_series. Read-only: writes, DDL, DROP, COPY, PRAGMA, ATTACH, and external-file table functions are rejected. System catalogs (information_schema, pg_catalog, sqlite_master, duckdb_*) are denied at the bridge layer — use bls_dataframe_describe to list available dataframes. Supports JOINs, aggregates, window functions, and CTEs. Optional register_as persists the result as a new dataframe with a fresh TTL for chained analysis. Canvas SQL operations consume zero BLS API quota. Requires CANVAS_PROVIDER_TYPE=duckdb.

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• openfda_dataframe_query

  openfda-mcp-server

  Run a read-only SQL SELECT against a DataCanvas table staged by an openFDA search tool (call one with stage=true; its response carries canvas_id + canvas_table). Enables GROUP BY, COUNT/SUM/AVG, time-series, and joins across the staged result set without re-paging the API. Call openfda_dataframe_describe first to get the exact table and column names. Results are capped at the canvas row limit — when truncated is true, page the rest with ORDER BY plus LIMIT/OFFSET. Scalar fields are stored as text (CAST for numeric math); nested objects/arrays are JSON columns — read them with DuckDB json functions, e.g. json_extract_string(openfda, '$.brand_name[0]'). Only SELECT is allowed — DDL, DML, COPY, and file-reading functions are blocked.

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• eia_dataframe_query

  eia-energy-mcp-server

  Run a single-statement SELECT against canvas dataframes registered by eia_query_route calls that passed stage: true — a query that staged nothing leaves no table to select from. Standard DuckDB SQL — joins, aggregates, window functions, CTEs all supported. Reference dataframes by the df_<id> handles returned by eia_query_route or listed by eia_dataframe_describe. Read-only: writes, DDL, DROP, COPY, PRAGMA, ATTACH, and external-file table functions are rejected. System catalogs (information_schema, pg_catalog, sqlite_master, duckdb_*) are denied. EIA data values are VARCHAR — use CAST(col AS DOUBLE) for arithmetic and aggregation. Optional register_as chains results as a new dataframe with a fresh expiry. Every dataframe named in the statement has its expiry extended by the query.

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• faostat_dataframe_query

  faostat-mcp-server

  Run a single-statement SELECT against the canvas tables staged by faostat_query_observations and faostat_commodity_profile (table names look like faostat_xxxxxxxx). Use this for cross-country and cross-item aggregation, GROUP BY rankings, joins, and time-series analysis over the full result set the inline preview only sampled. Standard DuckDB SQL — joins, aggregates, window functions, CTEs all work. Read-only: writes, DDL, DROP, COPY, PRAGMA, ATTACH, and external-file table functions are rejected; system catalogs (information_schema, sqlite_master, duckdb_*) are denied — list staged tables via faostat_dataframe_describe. Every row carries its data-quality `flag` — commonly A=Official, B=time-series break, E=Estimated, I=Imputed, M=Missing (value cannot exist), T=Unofficial, X=from an international organization, plus others FAOSTAT defines per domain — keep it in projections, treat any unrecognized flag as informational, and never assume it is official.

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• openfda_dataframe_describe

  openfda-mcp-server

  List the tables and column schemas on a DataCanvas staged by an openFDA search tool. Call before openfda_dataframe_query to discover the exact table name, column names, and DuckDB types needed for valid SQL. row_count is the full staged result set, not the inline preview count. Columns typed JSON hold nested openFDA objects/arrays — query them with DuckDB json functions.

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• socrata_dataframe_query

  socrata-mcp-server

  Run SELECT-only SQL against a DataCanvas table populated by socrata_query_dataset. DuckDB infers types from spilled data, so numeric columns that SODA returned as strings become queryable with numeric comparisons (year > 2020, amount < 500). Only works when CANVAS_PROVIDER_TYPE=duckdb is set. Use socrata_dataframe_describe to see registered tables and their schemas.

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• fema_dataframe_describe

  fema-mcp-server

  List tables and column schemas on a DataCanvas staged by fema_search_nfip. Call this before fema_dataframe_query to discover the exact table name, column names, and DuckDB data types needed to write valid SQL. Row count reflects what was actually staged — check truncated in the fema_search_nfip response to know whether the canvas holds the full matching set.

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• fx_dataframe_query

  exchange-rates-mcp-server

  Run a read-only SQL SELECT against DataCanvas tables staged by fx_get_timeseries. Supports aggregations, GROUP BY, window functions, and JOINs across multiple registered tables. Run fx_dataframe_describe first to discover table names and column schemas. Requires DataCanvas (CANVAS_PROVIDER_TYPE=duckdb) — without it this tool is not listed at all and fx_get_timeseries returns every range inline.

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• socrata_query_dataset

  socrata-mcp-server

  Execute a SoQL query against any dataset on any Socrata portal. Use the search parameter for quick full-text lookup, or combine select/where/group/having/order for full analytical control. Returns rows plus the assembled SoQL string so you can learn the pattern. All SODA 2.1 row values are strings even for numeric columns — check dataType from socrata_get_dataset to determine correct WHERE quoting: Number columns use bare literals (year=2023), Text columns use single-quoted strings (year='2023'). To enumerate distinct values, use select="col, count(*) as n" with group="col" and order="n DESC". When CANVAS_PROVIDER_TYPE=duckdb and rows fill the limit, results spill to a DataCanvas table for SQL-based analysis.

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• socrata_dataframe_describe

  socrata-mcp-server

  List registered tables in a DataCanvas session — schema, row count, column names, and registration time. Shows what datasets are available for SQL queries via socrata_dataframe_query. Only meaningful when CANVAS_PROVIDER_TYPE=duckdb is set. Use after socrata_query_dataset spills a large result set to canvas.

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• bls_dataframe_describe

  bls-labor-mcp-server

  List canvas dataframes materialized by bls_get_series, with provenance (source tool, query parameters), TTL, row count, and column schema. Use before writing SQL to confirm column names. Lazy-sweeps expired tables before responding. Requires CANVAS_PROVIDER_TYPE=duckdb.

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• treasury_query_dataset

  treasury-fiscaldata-mcp-server

  Query any Treasury Fiscal Data endpoint by path, field list, filters, sort, and page. Call treasury_list_datasets first to get the correct endpoint path and exact field names — a typo in either causes a 400. Filter syntax: each condition is { field, operator, value } where operator is eq/gt/gte/lt/lte/in (e.g., record_date:gte:2024-01-01). Multiple conditions are ANDed together. All response values are strings per the API contract, including numbers and dates; "null" (string) means no value. Supply canvas_id to register the page result into a named DataCanvas dataframe and query it later with treasury_dataframe_query (requires CANVAS_PROVIDER_TYPE=duckdb on the server).

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• fx_get_timeseries

  exchange-rates-mcp-server

  Get historical daily exchange rates for a currency pair over a date range. ECB publishes on business days only — weekends and holidays produce no entry, and no date outside the requested range is ever returned, so a range covering only non-publication days comes back with an empty rates map and a notice explaining why. A same-currency pair returns a rate of 1 on each publication day in the range. Short ranges (≤90 days by default) are returned inline as a date→rate map. When DataCanvas is enabled (CANVAS_PROVIDER_TYPE=duckdb) long ranges spill to it: the response carries spilled=true, a canvas_id, and a table_name — call fx_dataframe_describe to inspect the staged table, then fx_dataframe_query to run SQL against it. Without DataCanvas long ranges stay inline (spilled=false) and the notice says so.

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• fx_dataframe_describe

  exchange-rates-mcp-server

  List tables and columns staged on a DataCanvas from a prior fx_get_timeseries call. Required first step before fx_dataframe_query — use it to discover table names and column schemas. Requires DataCanvas (CANVAS_PROVIDER_TYPE=duckdb) — without it this tool is not listed at all and fx_get_timeseries returns every range inline.

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• execute_sql

  Energetica — Argentine Oil & Gas Data

  Execute arbitrary read-only SQL against the DuckDB database. Only SELECT and WITH statements are allowed. Use get_schema first to understand available tables and columns. Available on Professional tier and above.

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• get_compute_ready_stream

  Valuein — SEC EDGAR Fundamentals & Smart-Money Data

  Returns a short-lived (15-min) download URL for a bulk Parquet object that can be piped directly into Python/DuckDB/Polars for high-throughput computation that exceeds the MCP context window. The URL streams the object straight from Valuein storage and supports HTTP range reads, so `duckdb.read_parquet(url)` / `pl.read_parquet(url)` work without downloading the whole file first. Datasets: fact (per-entity partition — requires ticker), ratio (all computed ratios), valuation (DCF inputs), filing (SEC filing metadata), references (company universe), index_membership (historical index composition). Scoped to the caller's tier bucket; the link is signed and cannot be used to list the bucket or read other objects.

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• query_sql

  André — Analyse électorale française

  Exécute une requête SQL directe sur la base DuckDB (read-only). Tables principales : - general_results : résultats généraux par bureau de vote - candidats_results : résultats par candidat par bureau de vote - elections_disponibles : liste des élections (id_election, nb_bureaux, total_inscrits) - candidats_avec_nuances : candidats avec famille politique (si nuances enrichies) - resultats_par_famille : résultats agrégés par famille politique - communes, epci, departements, regions : divisions administratives - bv_sociodemographique : données socio-démo par bureau de vote ATTENTION — colonnes de general_results (noms avec espaces, à quoter) : id_election, id_brut_miom, "Code du département", "Code de la commune" (SANS préfixe dept, ex: '055' pour Marseille), "Libellé de la commune", Inscrits, Abstentions, Votants, Blancs, Nuls, Exprimés. PRÉFÉRER query_elections pour les analyses standard — query_sql est pour les requêtes ad hoc avancées. Exemple : SELECT id_election, ROUND(100.0 * SUM(Abstentions) / NULLIF(SUM(Inscrits), 0), 1) as abstention_pct FROM general_results WHERE "Code du département" = '13' AND "Code de la commune" = '055' GROUP BY id_election ORDER BY id_election DESC LIMIT 5

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• treasury_dataframe_describe

  treasury-fiscaldata-mcp-server

  List DataCanvas dataframes materialized by treasury_query_dataset, treasury_get_debt, treasury_get_interest_rates, and treasury_get_exchange_rates. Each entry surfaces source tool, query parameters, creation/expiry timestamps, row count, and column schema. Use this tool before treasury_dataframe_query to discover table names and column types. Requires CANVAS_PROVIDER_TYPE=duckdb.

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• imf_dataframe_describe

  imf-mcp-server

  List DataCanvas tables and columns staged by a prior imf_query_dataset call. Returns each table's name, row count, and column schema (name + DuckDB type). Required before imf_dataframe_query to discover the table and column names for SQL.

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• fema_search_nfip

  fema-mcp-server

  Search National Flood Insurance Program (NFIP) claims data by state, county, ZIP code, and year range. Returns claim counts, amounts paid on building and contents, flood zones, and loss years. state is required — the full NFIP dataset is 2.7 million rows; unfiltered access is prohibited. When DataCanvas is enabled (CANVAS_PROVIDER_TYPE=duckdb) and results exceed the inline preview, the full result set is staged on a canvas for SQL aggregation via fema_dataframe_query. Use fema_dataframe_describe to inspect the staged table schema before writing SQL. Without canvas, results are returned inline up to the limit.

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