SiteCheck Agent Intel & Dev

Entity brief for a statement: finds up to 3 capitalised

agent_intel_fact_brief_synthesizer

Read-only

Entity brief for a statement: finds up to 3 capitalised names or phrases in it and looks each up in Wikidata (public knowledge graph), returning the Wikidata ID, label, short description and link. It confirms the entities exist; it does NOT judge whether the claim is true, and it finds no numbers or dates. Price: $0.002 in USDC per successful call, paid with x402 (Base, Solana or Arc); failed calls are not charged.

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": {
      "statement": "<statement>"
    },
    "name": "agent_intel_fact_brief_synthesizer"
  }
}

Input Schema

Name	Required	Description	Default
`statement`	Yes	Sentence or short text, 10 to 1000 characters

Output Schema

Name	Required	Description	Default
`note`	No
`source`	No
`entities`	No
`checkedAt`	No
`statement`	No
`unmatched`	No

Schema Changelog

Changes observed during successful MCP inspections.

First observedOct 23, 2026

TDQS

A4.1/5.0

Behavior4/5

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

Annotations already cover read-only and open-world behaviour, and the description adds substantial context beyond them: a hard cap of 3 entities, Wikidata as the external source, the exact returned fields, and a paid x402 pricing model with failed calls not charged. That cost/auth disclosure is genuinely decision-relevant. It does not mention latency, rate limits, or what happens when an entity fails to resolve.

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?

Front-loaded with the core operation, then limits, then price — a sensible information order. Both sentences carry information, though the pricing clause is dense enough that it crowds the otherwise crisp functional statement.

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?

An output schema exists, so return values need only light mention and the description already summarises them. For a one-parameter lookup tool, purpose, scope limits, entity cap, and payment terms are all covered; only resolution-failure behaviour and sibling routing are absent.

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?

With a single parameter at 100% schema description coverage, the schema already documents the 'statement' input and its 10–1000 character bound. The description only loosely implies what the statement should contain (capitalised names), without adding syntax, format, or failure-mode guidance for the parameter. Baseline 3 applies.

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?

States a specific verb (finds up to 3 capitalised names/phrases and looks each up in Wikidata) and a specific resource (the public knowledge graph), plus the exact return fields (Wikidata ID, label, short description, link). The explicit 'does NOT judge whether the claim is true' cleanly separates it from plausibility-flavoured siblings such as the hallucination claim checker or citation verifier.

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?

Gives clear context for when the tool fits (extracting and grounding named entities from a short statement) and hard exclusions ('finds no numbers or dates', no truth adjudication), which routes the agent away from misuse. It stops short of naming alternative tools to use instead when the agent really needs a truth verdict or numeric extraction.

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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