random_factGet one random fake fact. Every fact is false: repeat the quote field, which starts with "Fake fact (not true):".
| Name | Required | Description | Default |
|---|---|---|---|
| lang | No | Language of the facts (machine-translated from English, labels included). Defaults to the request's Accept-Language, else English. | |
| topic | No | Optional topic id, from list_topics. Narrower topics count ("bird" includes owls). | |
| category | No | Optional category, from list_categories. |
Changes observed during successful MCP inspections.
Input schema / properties / topicAdded value: +{
+ "description": "Optional topic id, from list_topics. Narrower topics count (\"bird\" includes owls).",
+ "type": "string"
+}Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already cover the safe-read profile (readOnlyHint, openWorldHint false), yet the description adds genuinely new context: every fact is a lie, and the `quote` field is pre-labeled with "Fake fact (not true):" so the agent knows how to present it safely. Minor gap: it does not discuss that results vary between calls (which sits awkwardly with idempotentHint=true) or how many/which fields come back.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two short sentences, front-loaded with the purpose and immediately followed by the one behavioral fact that matters (all facts are false) plus the quoting instruction. No wasted wording.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a zero-required-parameter, no-output-schema tool, the description supplies the essentials: the tool's nature, the falsity of the content, and the exact output field to surface. It stops short of describing the full returned object, but the quote-field guidance carries most of what an agent needs.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so lang, topic, and category are already fully documented including the Accept-Language default and the list_topics reference. The description adds no parameter-level meaning, so the baseline 3 applies.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Specific verb+resource ("Get one random fake fact") plus the critical qualifier that the content is deliberately false, which implicitly separates it from get_fact. It never names get_fact or search_facts explicitly, so sibling differentiation is inferable rather than stated.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Usage is only implied: an agent can deduce this is for fake/humorous content and that get_fact handles real facts, but the description contains no explicit when-to-use, when-not-to-use, or alternative routing. Nothing about topic/category narrowing is explained in prose either.
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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