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Sora-bluesky

building-standards-act-mcp

by Sora-bluesky

引用検証

verify_citation
Read-onlyIdempotent

Validate legal citations in AI responses by checking article existence and matching claimed text against original Japanese building standards laws, with support for up to 10 citations.

Instructions

AIの回答に含まれる法令引用を検証する。条文の存在確認、テキスト照合による正確性チェックが可能。最大10件。

Input Schema

NameRequiredDescriptionDefault
citationsYes検証対象の引用リスト(最大10件)

Output Schema

NameRequiredDescriptionDefault
errorYes
totalYes
resultsYes
mismatchYes
verifiedYes
not_foundYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed3 schema fields changedv1.11.2
    • removedInput schema / additionalProperties
      Removed value: -false
    • removedInput schema / properties / citations / items / additionalProperties
      Removed value: -false
    • changedOutput schema / (root)
      Previous value: -nullNew value: +{
      +  "$schema": "http://json-schema.org/draft-07/schema#",
      +  "additionalProperties": false,
      +  "properties": {
      +    "error": {
      +      "type": "number"
      +    },
      +    "mismatch": {
      +      "type": "number"
      +    },
      +    "not_found": {
      +      "type": "number"
      +    },
      +    "results": {
      +      "items": {
      +        "additionalProperties": false,
      +        "properties": {
      +          "actual_text": {
      +            "type": "string"
      +          },
      +          "article_number": {
      +            "type": "string"
      +          },
      +          "error_message": {
      +            "type": "string"
      +          },
      +          "law_name": {
      +            "type": "string"
      +          },
      +          "match_score": {
      +            "type": "number"
      +          },
      +          "mismatch_detail": {
      +            "type": "string"
      +          },
      +          "status": {
      +            "enum": [
      +              "verified",
      +              "mismatch",
      +              "article_not_found",
      +              "law_not_found",
      +              "error"
      +            ],
      +            "type": "string"
      +          }
      +        },
      +        "required": [
      +          "law_name",
      +          "article_number",
      +          "status"
      +        ],
      +        "type": "object"
      +      },
      +      "type": "array"
      +    },
      +    "total": {
      +      "type": "number"
      +    },
      +    "verified": {
      +      "type": "number"
      +    }
      +  },
      +  "required": [
      +    "total",
      +    "verified",
      +    "mismatch",
      +    "not_found",
      +    "error",
      +    "results"
      +  ],
      +  "type": "object"
      +}
  2. First observedv1.7.0

TDQS

A3.8/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint and destructiveHint=false, so the safety profile is covered. The description adds genuinely new behavioral context beyond that: the batch ceiling of 10 items and the fact that accuracy checking is done via text matching (as opposed to mere existence lookup). It does not discuss failure behavior for unverifiable citations, which keeps it from a 5.

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?

Three short clauses, no filler, and the core action is front-loaded before the capability details and the limit. Every sentence carries information an agent needs.

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 an output schema present, the description needn't explain return values, and it covers purpose, both verification modes, and the batch limit for a single-parameter tool. Slightly incomplete on what happens when a citation cannot be verified, but nothing critical to correct invocation is missing.

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 100% for the single 'citations' parameter, including per-field descriptions and the omission semantics of claimed_text, so the baseline is 3. The description's only added datum is the repeated 10-item limit, which the schema already enforces via maxItems.

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 names a specific verb and resource (verify legal citations contained in AI answers) and enumerates the two verification modes: existence confirmation and text-matching accuracy. This clearly separates it from siblings like get_law or search_articles, which retrieve rather than verify. It falls short of 5 only because it never names an alternative sibling to route against.

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

Usage Guidelines3/5

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

Usage is implied by the phrase 'AIの回答に含まれる法令引用' — the tool is for checking citations produced by a model — but there is no explicit when-to-use/when-not guidance and no named alternative for adjacent tasks. An agent can infer the context but must guess at the boundary with analyze_article or get_law.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.