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faostat-mcp-server

by io.github.cyanheads

faostat-mcp-server: dataframe query

faostat_dataframe_query
Read-onlyIdempotent

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.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
sqlYesSingle-statement read-only SELECT against staged faostat_<id> tables. Columns: area_code, area, item_code, item, element_code, element, year, unit, value, flag. CAST(value AS DOUBLE) for arithmetic.
canvas_idNoOptional canvas ID from a prior faostat_query_observations / faostat_commodity_profile call. Omit to query the tables staged in this session (the common case).
row_limitNoHard cap on rows in the response. Default 1000, max 10000.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
rowsNoMaterialized result rows, bounded by row_limit.
errorNoPresent when the call failed. Absent on success.
noticeNoGuidance when the query returned no rows or when results were capped.
columnsNoColumn names in projection order.
row_countNoRows returned in this response — the materialized count, equal to rows.length. When truncated is true this is NOT the full result total (this path computes no exact total); page or aggregate to reach the rest.
truncatedNoTrue when row_limit capped the result and more rows exist than were returned. To reach them: page with ORDER BY + SQL LIMIT/OFFSET, raise row_limit (max 10000), or aggregate with GROUP BY.

TDQS

A4.6/5.0
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Beyond the readOnlyHint annotation, the description details enforcement: writes, DDL, DROP, COPY, PRAGMA, ATTACH, and external-file functions are rejected, and system catalogs are denied. It adds critical flag semantics (A, B, E, I, M, T, X) with a caution not to assume flags are official. This is rich, actionable disclosure that materially helps the agent.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is dense but well-structured: purpose first, then usage, restrictions, and flag guidance. Every sentence earns its place, though it is on the longer side. It is front-loaded and organized, but a slightly tighter phrasing could improve scannability.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (free-form SQL, read-only enforcement, flag fields), the description covers all operational essentials: table naming, allowed SQL, denied constructs, flag handling, and sibling tool references. An output schema exists, so return details are already specified elsewhere. No critical operational detail is missing.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so each parameter already has a description. The tool description adds only marginal param-specific nuance (e.g., column list and CAST hint) that is also present in the schema. It does not compensate beyond the baseline expected when structure is fully documented.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description opens with a specific verb+resource: 'Run a single-statement SELECT against the canvas tables staged by faostat_query_observations and faostat_commodity_profile'. It clearly distinguishes itself from siblings like faostat_dataframe_describe (for listing tables) and staging tools, and lists concrete use cases (aggregation, GROUP BY, joins, time-series). This leaves no ambiguity about what the tool does.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

It explicitly states when to use the tool ('Use this for cross-country and cross-item aggregation...') and points to an alternative for a different need ('list staged tables via faostat_dataframe_describe'). It also clarifies the common case for the canvas_id parameter and enumerates disallowed SQL constructs, giving clear boundaries for usage.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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TDQS

A4.4/5.0
Disambiguation5/5

Each tool has a clearly distinct purpose: discovery (list_domains), code resolution (resolve_codes), raw data query (query_observations), high-level profile assembly (commodity_profile), and dataframe management (describe/query). The composite profile tool overlaps slightly with query_observations, but its description clearly positions it as a convenience wrapper, eliminating ambiguity.

Naming Consistency4/5

All tools share the faostat_ prefix, and most use verb_noun structure (list_domains, query_observations, resolve_codes, dataframe_describe, dataframe_query). The exception is commodity_profile, which is a noun_phrase rather than verb-first, though it is still descriptive and follows the overall underscore convention. This minor deviation keeps the set mostly consistent.

Tool Count5/5

With 6 tools, the server is well-scoped for its purpose: it covers the essential workflow of discovering domains, resolving codes, querying data, and analyzing results via dataframes. Each tool has a clear role, and the count is appropriate for a focused statistical data server without excess or deficiency.

Completeness5/5

The tool surface covers the full data retrieval lifecycle: domain discovery, code resolution, raw data extraction, and flexible post-query analysis (including a high-level commodity profile shortcut). The descriptions handle edge cases like aggregation exclusion and flag validation, and no obvious gaps exist for a read-only FAOSTAT querying server.

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