"DuckDB" matching MCP tools:

• 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

• 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

• 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 29 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

• 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

• 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

• 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. 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.

  ConnectorNo auth

• socrata_dataframe_queryA

  socrata-mcp-serverA

  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.

  ConnectorNo auth

• fema_dataframe_describeA

  fema-mcp-serverA

  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.

  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

• bls_dataframe_queryA

  bls-labor-mcp-serverA

  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.

  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

• socrata_dataframe_describeA

  socrata-mcp-serverA

  List registered tables in a DataCanvas session — schema, row count, and column names. 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.

  ConnectorNo auth

• faostat_dataframe_queryA

  faostat-mcp-serverA

  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.

  ConnectorNo auth

• treasury_dataframe_describeA

  treasury-fiscaldata-mcp-serverA

  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.

  ConnectorNo auth

• fx_dataframe_describeA

  exchange-rates-mcp-serverA

  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.

  ConnectorNo auth

• fx_dataframe_queryA

  exchange-rates-mcp-serverA

  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.

  ConnectorNo auth

• chembl_dataframe_describeA

  chembl-mcp-serverA

  List the tables and columns staged on a canvas by chembl_get_bioactivities — inspect before calling chembl_dataframe_query to write correct SQL. Returns each table with its row count, kind (table | view), and column names + types. Requires CANVAS_PROVIDER_TYPE=duckdb.

  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

  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

• oecd_dataframe_describeA

  oecd-mcp-serverA

  List tables and columns staged on a DataCanvas by a prior oecd_query_dataset spill. Call this before oecd_dataframe_query to discover exact table and column names for SQL. Only available when CANVAS_PROVIDER_TYPE=duckdb is set.

  ConnectorNo auth