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io.github.cyanheads/faostat-mcp-server
UN FAOSTAT global food & agriculture statistics over a local SQLite mirror, via MCP.
biocontext
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glama
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mcp.so
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nerq
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pulsemcp
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smithery
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Assemble a global profile for one commodity in a single call: top-producing countries, the multi-decade production trend, and trade flows (top exporters and importers). Accepts a commodity name, resolves it to item codes, then queries the production (QCL) and trade (TCL) domains and merges the results. Country-level only (aggregates excluded). When a required domain is not indexed locally, returns a partial profile with a notice naming the gap rather than failing. The full merged observation set spills to a DataCanvas table for deeper SQL via faostat_dataframe_query.
List the canvas tables (faostat_xxxxxxxx) staged by faostat_query_observations and faostat_commodity_profile, each with its source tool, the query parameters that produced it, creation/expiry timestamps, row count, and column schema. Call this before faostat_dataframe_query to discover the exact table and column names to reference in SQL.
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` (A=Official, E=Estimated, I=Imputed, …) — keep it in projections and honor it in interpretation.
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