factanker

search_facts

Read-onlyIdempotent

Full-text search across entity names and predicates; returns the most recent matching evidence-backed facts incl. provenance. Use this ONLY when you cannot name the organization precisely — for example the user wrote a trade name, a misspelling or a partial phrase. If you already know the company, use lookup_entity and then get_facts: that path is exact, this one is a guess ranked by text similarity. Never present a search hit as the answer without checking that the entity name actually matches what was asked.

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": {
      "query": "<query>"
    },
    "name": "search_facts"
  }
}

Input Schema

Name	Required	Description	Default
`limit`	No
`query`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedAug 19, 2026

TDQS

A4.3/5.0

Behavior4/5

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

Annotations already cover the safety profile (readOnly, idempotent, closed-world), but the description adds genuinely non-structured behavior: results are ranked by text similarity, this path is a 'guess' rather than an exact lookup, and hits must be verified against the asked-for entity. It stops short of disclosing ordering/pagination or how ties are broken.

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?

Four sentences, all informative and front-loaded with the capability before the routing rules. Slightly dense, but no sentence is redundant; the verification caveat earns its place as a usage constraint.

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?

With no output schema, the description does describe returns (recent evidence-backed facts incl. provenance) and fully covers when to use vs. avoid it. The only omission is the limit parameter's behavior, which is the one gap an agent could need before invoking.

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 0%, so the description must carry the load. It clarifies what 'query' is matched against (entity names and predicates), which is real added meaning, but it says nothing about 'limit' — no default, no effect on result set, no relation to ranking.

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 (full-text search) over a specific resource (entity names and predicates) and describes the return (most recent matching evidence-backed facts with provenance). It also explicitly distinguishes itself from lookup_entity/get_facts, so an agent can route without opening another schema.

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?

Gives an explicit when-to-use condition ('ONLY when you cannot name the organization precisely'), concrete triggers (trade name, misspelling, partial phrase), and names the alternative path (lookup_entity then get_facts) with the reason it is preferred (exact vs. guess). It even adds a post-call verification 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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