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Read-onlyIdempotent

Full-text search over the knowledge graph. Matching ignores accents and apostrophes, so query in the user's own words; every hit carries the fields it matched and a score. BM25 relevance: each query term is weighted by how RARE it is in this corpus and by where it hits (name 3, tags 2, questions 2, body 1). A hit must also cover a minimum share of the question's information, measured in the same rarity weights — matching only common words does not qualify. Centrality (how many objects point at this one) breaks TIES ONLY and is never part of the score, so it cannot make an irrelevant object rank. Two hits with the same matched_fields can still differ: the score is rarity-weighted, so matching a rare term is worth more than matching a common one. Use this whenever you have a question rather than an id, then follow up with get_entity.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoHow many ranked hits to return, best first.
queryYesWhat to look for, in the user's own words. Matched against titles, questions, claim text and tags; accents and apostrophes are ignored, so 'jose' finds 'José'.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
totalYes
resultsYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed5 schema fields changed
    • addedOutput schema / properties / results / items / properties / coverage
      Added value: +{
      +  "description": "Share of the question's information this object accounts for, weighted by term rarity. 1 means every meaningful word in the query is present; a low value with a high score means the object matched common words only.",
      +  "type": "number"
      +}
    • changedOutput schema / properties / results / items / properties / matched_fields / description
      Previous value: -"Which fields the query hit: name, tags, content. Does NOT determine the score on its own."New value: +"Which fields the query hit: name, tags, questions, summary, claims. Does NOT determine the score on its own."
    • changedOutput schema / properties / results / items / properties / score / description
      Previous value: -"Text match, weighted by where the token hits (name 3, tags 2, content 1 per query token), plus a centrality bonus of up to 0.9 for objects that other objects point at. That bonus is why two hits can share matched_fields and still score differently: the decimal is inbound edges, not text relevance."New value: +"BM25 relevance: each query term is weighted by how RARE it is in this corpus and by where it hits (name 3, tags 2, questions 2, body 1). A hit must also cover a minimum share of the question's information, measured in the same rarity weights — matching only common words does not qualify. Centrality (how many objects point at this one) breaks TIES ONLY and is never part of the score, so it cannot make an irrelevant object rank. Two hits with the same matched_fields can still differ: the score is rarity-weighted, so matching a rare term is worth more than matching a common one."
    • addedOutput schema / properties / results / items / properties / x
      Added value: +{
      +  "additionalProperties": {
      +    "additionalProperties": {},
      +    "propertyNames": {
      +      "type": "string"
      +    },
      +    "type": "object"
      +  },
      +  "description": "Indexed instance-specific attributes, grouped by namespace — the same fields api/index.json publishes. Absent when the instance declares none.",
      +  "propertyNames": {
      +    "type": "string"
      +  },
      +  "type": "object"
      +}
    • changedOutput schema / properties / results / items / required
      Previous value: -[
      -  "id",
      -  "type",
      -  "name",
      -  "summary",
      -  "tags",
      -  "evidence_tier",
      -  "confidence",
      -  "last_verified",
      -  "canonical_url",
      -  "api_url",
      -  "score",
      -  "matched_fields"
      -]New value: +[
      +  "id",
      +  "type",
      +  "name",
      +  "summary",
      +  "tags",
      +  "evidence_tier",
      +  "confidence",
      +  "last_verified",
      +  "canonical_url",
      +  "api_url",
      +  "score",
      +  "matched_fields",
      +  "coverage"
      +]
  2. Changed4 schema fields changed
    • addedInput schema / properties / limit / description
      Added value: +"How many ranked hits to return, best first."
    • addedInput schema / properties / query / description
      Added value: +"What to look for, in the user's own words. Matched against titles, questions, claim text and tags; accents and apostrophes are ignored, so 'jose' finds 'José'."
    • addedOutput schema / properties / results / items / properties / matched_fields / description
      Added value: +"Which fields the query hit: name, tags, content. Does NOT determine the score on its own."
    • addedOutput schema / properties / results / items / properties / score / description
      Added value: +"Text match, weighted by where the token hits (name 3, tags 2, content 1 per query token), plus a centrality bonus of up to 0.9 for objects that other objects point at. That bonus is why two hits can share matched_fields and still score differently: the decimal is inbound edges, not text relevance."
  3. First observed

TDQS

A4.9/5.0
Behavior5/5

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

Even though annotations already declare readOnlyHint and idempotentHint, the description adds substantial behavioral detail beyond them: accent/apostrophe normalization, matched-field reporting, BM25 rarity weighting, per-field weights, minimum information coverage, and centrality's strictly tie-breaking role. This is rich, non-obvious behavior an agent could not infer from annotations or schema.

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 front-loaded with the core purpose and then builds each subsequent sentence on that foundation. Every sentence earns its place: normalization rules, result content, scoring mechanics, tie-breaking, and usage routing. Despite its length, there is no filler or repetition.

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

Completeness5/5

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

The description is complete for an agent to decide when to call search and how to use it correctly, given that an output schema exists for return values and annotations cover safety. It explains the ranking model, matches, tie-breaking, and follow-up workflow, leaving no meaningful gap for this read-only tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so the schema already documents both query and limit. The description adds meaningful semantic value beyond the schema by explaining that queries should use the user's own words, that accents and apostrophes are ignored, and that scoring depends on rarity and hit location—information that materially affects how an agent should construct queries.

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?

The description opens with a specific verb and resource: 'Full-text search over the knowledge graph.' It clearly states what the tool does, differentiates it from id-based retrieval via the instruction to follow up with get_entity, and describes the matching behavior, so an agent can distinguish it from siblings without opening schemas.

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?

The description gives explicit usage guidance: 'Use this whenever you have a question rather than an id, then follow up with get_entity.' This names the relevant alternative and the condition that selects search instead, which is exactly the kind of when-to-use versus when-not-to-use guidance needed.

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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