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UNESCO UIS — Education, Science & Culture Statistics (provenance-first)

by io.github.SidneyBissoli

Deep Research Document

fetch
Read-onlyIdempotent

Returns the full document for an id obtained from search, as { id, title, text, url, metadata }: text is the readable content (Markdown) and url the canonical public page to cite.

Companion of search in the OpenAI Deep Research contract, over the UNESCO UIS statistics (≈5,000 indicators: education — enrolment, completion, literacy, teachers, spending, SDG 4 —, science/R&D (SDG 9.5), culture (SDG 11.4) and demographic context) catalog. Only ids returned by search are valid; an unknown id returns an error. The uis_* tools remain the tools for data queries.

Behavior: read-only and idempotent — a live GET against the public source when the document needs it.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
idYesIdentifier of a document returned by `search`

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
idYesUnique identifier of the document on this server; what `fetch` takes
urlYesCanonical public URL of the document — ChatGPT's citation depends on it
textYesFull readable content of the document (Markdown)
titleYesHuman-readable title of the document
metadataNoAdditional key/value pairs about the document (kind, source, period…)
provenanceYes
attributionYes

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Added

TDQS

A4.8/5.0
Behavior5/5

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

Annotations already declare readOnlyHint and idempotentHint; the description adds beyond that by explaining the live GET behavior against the public source and the error condition for unknown ids. This gives the agent practical expectations without contradicting the annotations.

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 front-loaded with the primary action and return contract, followed by domain context, error behavior, and routing guidance. Each sentence provides distinct value, though the catalog context sentence adds length and density.

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?

For a one-parameter fetch tool with an output schema, the description covers the return shape, id provenance, error behavior, behavioral traits, and sibling distinctions. Nothing needed for the agent to invoke it correctly is missing.

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

Parameters4/5

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

Schema coverage is 100% with the id description, so the baseline is 3. The description adds meaningful extra context by stating that only ids from `search` are valid and that an unknown id errors, reinforcing validation rules beyond the schema's simple field description.

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 and resource: 'Returns the full document for an id obtained from `search`', and details the exact return shape ({ id, title, text, url, metadata }). It also distinguishes itself from the `uis_*` data query tools, so an agent can immediately tell this is the document-retrieval companion to `search`.

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 that only ids returned by `search` are valid and that unknown ids return an error, giving clear input constraints. It also directs data queries to the `uis_*` tools, providing an unambiguous 'when not to use this tool and what to use instead' rule.

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

A4.6/5.0
Disambiguation4/5

The uis_* tools form a clearly separated data workflow: search indicators, list geo units, and get data each have distinct roles, while search/fetch are a distinct document-retrieval pair. The generic names search and fetch could be confused with uis_search_indicators and uis_get_data at first glance, but the descriptions explicitly clarify the boundary.

Naming Consistency4/5

The three data tools follow a consistent uis_<verb>_<noun> pattern, while search and fetch are bare verbs tied to the Deep Research contract. This is a visible naming deviation, but the two groups are internally consistent and the prefix convention remains strong.

Tool Count5/5

Five tools is well-scoped for this server: two document-retrieval tools for the Deep Research contract and three tightly linked data tools for indicator discovery, geographic lookup, and data extraction. None of the tools feel redundant or excessive.

Completeness5/5

The tool surface covers the full query workflow: find indicators, resolve geo units, retrieve data with provenance, and fetch full documents from search results. There are no dead ends or obvious missing operations for the stated statistics domain.

Resources