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Citation Verification

by io.github.guptaprakhariitr
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fact_check

Verify a factual claim against authoritative sources (Wikipedia, Wikidata, Crossref academic citations). Returns a verdict ('supported' / 'contradicted' / 'mixed' / 'unverified'), a 0–1 confidence score, and the list of sources with excerpts. Use this when an agent is about to assert a fact it isn't 100% sure of, or when post-processing LLM output to flag possibly-hallucinated claims.

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": {
      "claim": "<claim>"
    },
    "name": "fact_check"
  }
}

Input Schema

NameRequiredDescriptionDefault
claimYesA single factual claim, ideally one sentence. Longer text is truncated to 200 chars.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4/5.0
Behavior4/5

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

The description discloses key behaviors: it returns a verdict, confidence score, and list of sources with excerpts. It also mentions that longer claims are truncated to 200 characters. Since no annotations are provided, the description adequately covers the behavioral traits, though it could mention any rate limits or authentication requirements if applicable.

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

Conciseness5/5

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

The description is concise, consisting of three front-loaded sentences. Each sentence adds unique value: purpose, return values, and usage guidance. There is no redundancy or wasted words.

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?

For a simple tool with one required parameter and no output schema, the description is complete. It explains the tool's function, return values, and when to use it. It could briefly mention error handling or edge cases for completeness, but it is largely adequate.

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?

The input schema covers the single parameter 'claim' with a clear description. The tool description does not add additional semantic context beyond what the schema already provides. With 100% schema coverage, a baseline score of 3 is appropriate.

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 description clearly states the tool's purpose: verify a factual claim against authoritative sources (Wikipedia, Wikidata, Crossref). It lists the verdict types and sources, making the purpose specific. However, it does not explicitly differentiate from sibling tools like cite_check or source_freshness, which could clarify when this tool is the best choice.

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

Usage Guidelines4/5

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

The description provides explicit guidance on when to use the tool: when an agent is unsure about a fact or when post-processing LLM output to detect hallucinated claims. This is clear and actionable. It does not specify when not to use it or mention alternatives, but the guidance is still strong.

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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