"DuckDB" matching MCP tools:

• bls_dataframe_queryA

  bls-labor-mcp-serverA

  Run a single-statement SELECT against the canvas dataframes registered by bls_get_series or by an earlier register_as. 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.

  ConnectorNo auth

• chembl_dataframe_queryA

  chembl-mcp-serverA

  Run a read-only SQL SELECT over the bioactivity rows chembl_get_bioactivities spilled to a canvas — rank, group, dedupe, and aggregate across the FULL set, not the inline preview. Reference each staged table by the name chembl_get_bioactivities returned — bioactivities for its potency_ranked view, bioactivities_null_potency for null_potency; discover the staged tables and their columns with chembl_dataframe_describe. Compute honest aggregates here (e.g. SELECT molecule_chembl_id, MEDIAN(pchembl_value) AS med FROM bioactivities WHERE standard_type = 'IC50' GROUP BY 1 ORDER BY 2 DESC). Two independent bounds apply, each reported on its own field: truncated is true when the SQL result exceeded the canvas row cap, and rendered_rows says how many of the returned rows the markdown table holds once its character budget is reached (below row_count on a wide or long result). Page past either bound with SQL LIMIT/OFFSET — append e.g. LIMIT 500 OFFSET 500 and re-call; offsets reach rows beyond the canvas row cap. Requires CANVAS_PROVIDER_TYPE=duckdb.

  ConnectorNo auth

• chembl_get_bioactivitiesA

  chembl-mcp-serverA

  The flagship compound↔target bioactivity bridge: measurements for a molecule (target deconvolution / selectivity), a target (lead finding), or both together (how potently one compound hits one target). Supply at least one of molecule_chembl_id (from chembl_search_molecules) or target_chembl_id (from chembl_search_targets) — supplying both narrows to that compound–target pair, supplying neither is an error. Filter by standard_type (IC50/Ki/EC50/…), minimum potency pchembl_value_min, assay_type, and organism. Not every measurement has a derivable pchembl_value, so potency_view picks which side of that split you get: the default "potency_ranked" returns the measurements that have one, most potent first (ChEMBL sorts the rest first otherwise, which is why they are not merged), and "null_potency" returns exactly the measurements that have none. totalCount is the honest full match count across both views either way. Mixing measurement types (IC50 vs Ki) is a scientific error — set standard_type to compare like with like. A popular target carries tens of thousands of rows: results spill to a DataCanvas table (call chembl_dataframe_describe for its columns, then chembl_dataframe_query for honest aggregates across the staged set), while an inline preview answers the immediate question. Each view stages its own table (bioactivities / bioactivities_null_potency), so running both against one canvas_id lets a UNION ALL rebuild the full set. The staged table is capped at CHEMBL_MAX_SPILL_ROWS; when the cap is hit, truncated is true and the table is a bounded slice, not the complete view. The inline rows are always capped at limit, so compare that against totalCount before treating them as the whole answer. Spilling the rest requires CANVAS_PROVIDER_TYPE=duckdb; without it the inline preview is all there is.

  ConnectorNo auth

• export_datasetA

  Misata Studio: verified synthetic dataA

  Export a generated dataset as a file. Returns a `download_url` the person can open (or you can fetch, e.g. with curl) for as long as the dataset is held, about 2 hours. Give the person the link rather than pasting file contents into the chat. Args: dataset_id: from a prior generate_dataset call. format: data: csv, parquet, jsonl, json, avro, xlsx, feather, orc, sqlite, duckdb, sql. code and docs: dbt, notebook, dictionary, dbml, mermaid, prisma, sqlalchemy, typescript, jsonschema, expectations, django, openapi, mockapi, demo. `sql` is schema.sql (DDL with keys) + data.sql (COPY/INSERT) — the way to seed a real database: run the returned SQL through your own database connection, since this server never holds a database credential itself. dialect: for `sql` only: postgres, mysql, sqlite, mssql, oracle, bigquery, snowflake. inline: also return the file itself as `base64` (only for files under a few MB). Use it when you must write the file yourself and cannot fetch a URL. Returns: filename, content_type, bytes, download_url, expires_at (unix seconds), and `base64` when `inline` and small enough.

  ConnectorNo auth

• socrata_query_datasetA

  socrata-mcp-serverA

  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. Columns are referenced by API field name (field_name from socrata_get_dataset, e.g. cuisine_description), never the display label. All SODA 2.1 row values are strings even for numeric columns — check data_type 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 limit, up to 50,000 matching rows spill to a DataCanvas table whatever the limit: list its columns with socrata_dataframe_describe, then run SQL with socrata_dataframe_query.

  ConnectorNo auth

• treasury_query_datasetA

  treasury-fiscaldata-mcp-serverA

  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 stage the page result as a DataCanvas table — read its column schema with treasury_dataframe_describe, then run SQL over it with treasury_dataframe_query (requires CANVAS_PROVIDER_TYPE=duckdb on the server).

  ConnectorNo auth

• query_datasetA

  Drupal Code QueryA

  Run read-only DuckDB SELECTs over the dataset behind the other tools, for a question none of them asks. Call describe_dataset first; it lists the 33 views, their columns, joins and recipes. Prefer a typed tool when one fits. - statements=[…]: up to 5 statements in one call, one result or error each. - Result: columns, and rows as arrays, up to max_rows (≤ 500, default 100) and 16 KB. When truncated is true: aggregate, filter, or use LIMIT and OFFSET. One SELECT (or SHOW, DESCRIBE, FROM-first), no semicolon, 15 s limit, nothing outside the bundle. - Dev-branch isolation: JOIN contrib_branch and filter kind = 'dev_branch' AND project <> 'drupal' before counting projects. change_record_adoption, symbol_usage and core_symbol_evidence hold release tags too. core_symbol_evidence is the full rollup; symbol_usage is its string-scan subset. - Adoption polarity: legacy is still on the old API (not adopted); migrated is adopted. Versions are text: compare *_seq integers (major*1000+minor). Never SUM(usage) across branch rows. - Errors list the views, the columns of the views you used, or the join map. An empty result over an fqn without a leading backslash gets a hint. - The same views are downloadable as parquet under https://api.tresbien.tech/data/docs. Its cookbook targets api.duckdb plus prelude views this mirror does not have, so take recipes from describe_dataset.

  ConnectorNo auth

• openfda_dataframe_queryA

  openfda-mcp-serverA

  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.

  ConnectorNo auth

• socrata_dataframe_queryA

  socrata-mcp-serverA

  Run SELECT-only SQL against a DataCanvas table populated by socrata_query_dataset. Columns SODA types as number (including aggregate aliases like count(*) as n) are staged as DOUBLE, so numeric comparisons work without a cast (year > 2020, amount < 500). Text and timestamp columns stay VARCHAR — compare times with CAST(date AS TIMESTAMP). Only works when CANVAS_PROVIDER_TYPE=duckdb is set. Use socrata_dataframe_describe to see registered tables and their schemas.

  ConnectorNo auth

• fema_search_nfipA

  fema-mcp-serverA

  Search National Flood Insurance Program (NFIP) claims data by state, county, ZIP code, and year range. Returns the matching claim count and claim records — amounts paid on building and contents, damage estimates, flood zones, cause and occupancy codes, and loss dates — newest loss first. state is required — the full NFIP dataset is 2.7 million rows; unfiltered access is prohibited. Page the inline claims with limit and offset; a page holds at most 100,000 characters of claims. When DataCanvas is enabled (CANVAS_PROVIDER_TYPE=duckdb) and the match exceeds that inline budget, a call at offset 0 stages the match (up to 50,000 claims) on a canvas: inspect the staged table with fema_dataframe_describe, then aggregate it with SQL via fema_dataframe_query.

  ConnectorNo auth

• fx_get_timeseriesA

  exchange-rates-mcp-serverA

  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. Inline results are returned as a date→rate map paged at 500 publication days: rate_count is always the total for the requested range, and when a page is cut short the response carries truncated=true and next_start_date — call again with start_date set to next_start_date and the same end_date for the next page. When DataCanvas is enabled (CANVAS_PROVIDER_TYPE=duckdb) long ranges (>90 days by default) spill to it instead: 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 are paged inline (spilled=false) and the notice says so.

  ConnectorNo auth

• transpile_sqlA

  ai-netcafeA

  Convert a SQL statement from one dialect to another — mysql, postgres, sqlite, tsql, oracle, snowflake, bigquery, redshift, spark, hive, presto, trino, duckdb, clickhouse, databricks, doris, starrocks and more. Deterministic parser (sqlglot), not an LLM: the same input always produces the same output, and syntax errors come back with the exact line and column. Use it when migrating queries between databases or debugging dialect-specific syntax.

  ConnectorNo auth

• transpile_sqlA

  AI NetCaféA

  Convert a SQL statement from one dialect to another — mysql, postgres, sqlite, tsql, oracle, snowflake, bigquery, redshift, spark, hive, presto, trino, duckdb, clickhouse, databricks, doris, starrocks and more. Deterministic parser (sqlglot), not an LLM: the same input always produces the same output, and syntax errors come back with the exact line and column. Use it when migrating queries between databases or debugging dialect-specific syntax.

  ConnectorNo auth

• transpile_sqlA

  netcafe-live-dataA

  Convert a SQL statement from one dialect to another — mysql, postgres, sqlite, tsql, oracle, snowflake, bigquery, redshift, spark, hive, presto, trino, duckdb, clickhouse, databricks, doris, starrocks and more. Deterministic parser (sqlglot), not an LLM: the same input always produces the same output, and syntax errors come back with the exact line and column. Use it when migrating queries between databases or debugging dialect-specific syntax.

  ConnectorNo auth

• transpile_sqlA

  netcafe-devkitA

  Convert a SQL statement from one dialect to another — mysql, postgres, sqlite, tsql, oracle, snowflake, bigquery, redshift, spark, hive, presto, trino, duckdb, clickhouse, databricks, doris, starrocks and more. Deterministic parser (sqlglot), not an LLM: the same input always produces the same output, and syntax errors come back with the exact line and column. Use it when migrating queries between databases or debugging dialect-specific syntax.

  ConnectorNo auth

• oecd_dataframe_queryA

  oecd-mcp-serverA

  Run a read-only SQL SELECT against OECD observation tables staged on a DataCanvas by oecd_query_dataset. Call oecd_dataframe_describe first to discover exact table and column names, then use this tool for aggregation, filtering, GROUP BY, JOIN, and window functions. Only available when CANVAS_PROVIDER_TYPE=duckdb is set.

  ConnectorNo auth

• eia_dataframe_queryA

  eia-energy-mcp-serverA

  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.

  ConnectorNo auth

• gnomad_list_gene_variantsA

  gnomad-genetics-mcp-serverA

  List every gnomAD variant in a gene, transcript, or region with allele frequencies and predicted consequences, optionally filtered to one consequence class (lof, missense, synonymous, other) and/or a maximum allele frequency. A result too large to inline is staged on a DataCanvas table named gene_variants, returned as canvas_id and table_name beside an inline preview — call gnomad_dataframe_describe for its columns, then gnomad_dataframe_query to rank by AF, count by consequence, or group across every row rather than the preview. A result that fits inline stages no table unless canvas_id is supplied. When the canvas is disabled (CANVAS_PROVIDER_TYPE != duckdb) the tool returns a capped inline preview and the SQL path is unavailable. Supply exactly one of gene, transcript_id, or region. Echoes the effective dataset and build. Data source: gnomAD (Broad Institute) — https://gnomad.broadinstitute.org/

  ConnectorNo auth

• openfda_dataframe_describeA

  openfda-mcp-serverA

  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.

  ConnectorNo auth

• imf_query_datasetA

  imf-mcp-serverA

  Query an IMF SDMX dataflow by dimension key over a time range. Returns observations with time_period, value, and status, plus the unit, scale, and decimals of each series — a key resolving to several series carries one entry per series in series_metadata, since unit and scale differ between them. Requires imf_get_database first to obtain the correct key_format and valid dimension codes. Country codes are ISO 3-letter (USA, GBR, DEU — not US, GB, DE). Key format: dot-separated codes in DSD keyPosition order (e.g. USA.NGDP_RPCH.A for WEO). Every position must carry a code: use + to combine codes (e.g. USA+GBR.NGDP_RPCH.A) and * to match every code at a position (e.g. *.NGDP_RPCH.A for all countries); * stands alone and cannot join a + list. Codes are matched case-insensitively and checked against each dimension's codelist before the query — an unknown code is rejected with the nearest valid codes. Codelists from imf_get_database enumerate the code universe, not actual coverage — valid codes can still return no_data if the combination has no series. start_period and end_period must be valid period strings (YYYY, YYYY-SN, YYYY-QN, YYYY-MM, or a calendar-valid YYYY-MM-DD) with start_period no later than end_period; malformed or reversed ranges are rejected. A bound covers the whole period it names, so end_period 2023 includes 2023-M12 and 2023-Q4. last_n_observations keeps each series' last N observations — 1 is each series' latest value, the cheap way to ask many countries for their latest figure. Values are in base units everywhere, staged canvas rows included: scale is the power of ten the IMF publishes a series in (value / 10^scale is the published figure), never a factor still to apply. A response is held to 100,000 serialized characters. With DataCanvas enabled (CANVAS_PROVIDER_TYPE=duckdb), a larger result (multi-country, long time range) spills to it: call imf_dataframe_describe first to inspect staged tables and columns, then imf_dataframe_query for SQL analysis. Without DataCanvas, a larger result returns its earliest observations with truncated=true, and retrieval_guidance names the last period returned and how to narrow the query.

  ConnectorNo auth

• imf_dataframe_describeA

  imf-mcp-serverA

  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.

  ConnectorNo auth