uis-mcp-server

Deep Research Document

fetch

Read-onlyIdempotent

Retrieves the full document for a search result ID, providing readable Markdown text, title, metadata, and canonical public URL to cite.

Instructions

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

Name	Required	Description	Default
`id`	Yes	Identifier of a document returned by `search`

Output Schema

TableJSON Schema

Name	Required	Description
`id`	Yes	Unique identifier of the document on this server; what `fetch` takes
`url`	Yes	Canonical public URL of the document — ChatGPT's citation depends on it
`text`	Yes	Full readable content of the document (Markdown)
`title`	Yes	Human-readable title of the document
`metadata`	No	Additional key/value pairs about the document (kind, source, period…)
`provenance`	Yes
`attribution`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.4.0Sep 17, 2026

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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