Only Fake Facts

Get a feature

get_article

Read-onlyIdempotent

Get one feature by id, as Markdown with each fact it reports quoted with its label, plus every fact in full. The feature's own prose adds no claims; all of its facts are false.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "id": "<id>"
    },
    "name": "get_article"
  }
}

Input Schema

Name	Required	Description	Default
`id`	Yes	Feature id, from list_articles.
`lang`	No	Language of the facts (machine-translated from English, labels included). Defaults to the request's Accept-Language, else English.

Output Schema

Name	Required	Description
`lang`	No
`note`	No
`site`	No
`error`	No
`article`	No
`articles`	No
`disclaimer`	Yes	Every fact is false.
`schema_version`	No

Schema Changelog

Changes observed during successful MCP inspections.

AddedNov 7, 2026

TDQS

B3.4/5.0

Behavior3/5

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

Annotations already cover readOnly, idempotent, and closed-world, so the bar is lower. The description does add genuinely new behavioral detail (Markdown rendering, each fact quoted with its label, full fact payloads) and a trust caveat, but the caveat is phrased ambiguously ("adds no claims; all of its facts are false"), leaving the agent unsure whether the data is unreliable or simply that prose is non-authoritative.

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

Conciseness3/5

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

It is short and front-loads the purpose in the first clause, which is good. The second sentence, however, is convoluted and self-contradictory on a first read, so it consumes space without delivering a crisp, actionable warning.

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

Completeness4/5

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

The tool is simple (one required id plus an optional language), an output schema exists so return structure needn't be explained, and the description covers purpose plus output format. The only real gap is the unresolved ambiguity of the trust warning, which an agent would want resolved before relaying content to a user.

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 description coverage is 100%, so both id and lang are already fully documented in the schema, including the enum and Accept-Language default. The description adds nothing about either parameter beyond "by id", which is a baseline-3 situation.

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

Purpose4/5

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

The first clause names a specific verb and resource ("Get one feature by id") and signals a single-item lookup distinct from list_articles. The clarity is slightly undercut by the name/title/schema mismatch (get_article vs. "feature" vs. "Feature id"), and it never explicitly contrasts with get_fact, which returns a single fact rather than the whole record.

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

Usage Guidelines3/5

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

Usage is only implied: "by id" suggests a point lookup after enumerating, and the schema's id description points at list_articles, but the description itself gives no explicit when-to-use, no when-not-to-use, and no comparison to get_fact or list_articles. An agent must infer the routing.

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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