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yamayued

houjin-bangou-api-mcp

by yamayued
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TypeScript
Remote

Server Quality Checklist

58%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v0.1.0

  • Disambiguation5/5

    Each tool addresses a distinct operation: exact lookup by corporate number, search by name, and retrieval of updates over a date range. There is no overlap in purpose, making it easy for an agent to select the correct tool.

    Naming Consistency5/5

    All tool names follow a clear verb_noun pattern with snake_case (get_/search_), and the suffix indicates the query parameter (by_number, by_name, updates). Minor pluralization variation (corporation vs. corporations) is semantically meaningful and does not detract from consistency.

    Tool Count5/5

    With exactly three tools, the server is tightly scoped to the three main endpoints of the Corporate Number API. Each tool earns its place, and the count is well within the ideal range for a focused MCP server.

    Completeness5/5

    The tool set fully covers the public surface of the National Tax Agency Corporate Number API: lookup by number, search by name, and updates. There are no obvious missing operations for common use cases, and agents can accomplish all typical tasks without workarounds.

  • Average 2.9/5 across 3 of 3 tools scored.

    See the Tool Scores section below for per-tool breakdowns.

    • No community issues in the last 6 months
    • 0 commits in the last 12 weeks
    • No stable releases found
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI is passing

  • This repository is licensed under MIT License.

  • This repository includes a README.md file.

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How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

  • Behavior2/5

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

    No annotations are provided, so the description carries full burden for behavioral disclosure. It only mentions the official API, but doesn't describe read-only nature, pagination (divide), return formats, or rate limits. Too little for a complex search tool.

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

    Conciseness3/5

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

    The description is a single clear sentence with no redundancy, but it is under-specified for a tool with 11 parameters. It is concise but not appropriately structured to convey necessary information.

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

    Completeness1/5

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

    Given the tool's complexity (11 params, no output schema, no annotations), this one-line description is severely insufficient. It fails to mention response types, pagination, or any operational details, making it incomplete for an agent to use correctly.

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

    Parameters1/5

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

    Schema description coverage is low (45%), and the description adds no parameter-level meaning. It doesn't explain any of the 11 parameters beyond the trivial fact that name is used, which is already in the required schema.

    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 searches corporations by name, using a specific verb 'search' and resource 'corporations'. It implicitly distinguishes from get_corporation_by_number (search by number) and get_corporation_updates (updates), though it doesn't explicitly name alternatives.

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

    Usage Guidelines2/5

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

    No guidance is provided on when to use this tool versus sibling tools. The phrase 'by name' weakly implies a use case, but there are no explicit context signals, prerequisites, or exclusions.

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

  • Behavior2/5

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

    With no annotations, the description carries full responsibility for disclosing behavior. It only says 'Fetch corporation details,' which is generic and fails to mention capabilities like handling multiple corporate numbers, response format options, or historical record retrieval. This is a significant transparency gap.

    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?

    The description is a single, concise sentence with no filler. It is front-loaded with the action and resource. While minimal, it is appropriately sized for a simple fetch tool, though it could be more informative without becoming verbose.

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

    Completeness2/5

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

    Given the tool's four parameters, no output schema, and sibling tools, this description is incomplete. It fails to explain when to use this tool (by corporate number) versus searching by name, the meaning of the responseType and history parameters, or any return value details. The sparse description leaves too much to be inferred.

    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 75%, so most parameters are explained there. However, the description itself adds no parameter context, and the corporateNumber parameter lacks a schema description. Baseline 3 is appropriate since the schema does most of the work.

    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 action ('Fetch') and the resource ('corporation details') from a specific source ('National Tax Agency Corporate Number API'). It is unambiguous, though it doesn't explicitly distinguish itself from sibling tools like search_corporations_by_name.

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

    Usage Guidelines2/5

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

    No usage guidance is provided. The description does not mention scenarios where this tool should be used over alternatives, nor does it reference sibling tools. The agent is left to infer from the tool name and siblings.

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

  • Behavior2/5

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

    With no annotations provided, the description carries the full burden. It discloses the basic read operation but fails to mention response formats, pagination, rate limits, or any other behavioral traits. The responseType parameter hints at format, but the description does not elaborate.

    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 a single, front-loaded sentence with no unnecessary words. Every phrase contributes to understanding the core function.

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

    Completeness2/5

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

    Given six parameters, no output schema, and no annotations, a one-sentence description is inadequate. It omits return format details, parameter semantics, and any practical usage context, leaving the agent under-informed for invocation.

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

    Parameters2/5

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

    Schema description coverage is only 17%, leaving five of six parameters undocumented. The description adds minimal value: it mentions 'date range' which maps to from/to, but does not explain kinds, divide, address, or responseType meanings, failing to compensate for the low schema coverage.

    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 clearly states the verb 'Fetch' and the resource 'corporations updated within a date range', which distinguishes it from sibling tools that fetch by number or name. The scope is specific and unambiguous.

    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?

    The date-range scope implies when to use this tool, but there is no explicit guidance on alternatives or exclusions. Sibling tools are not referenced, though their existence is evident from the context signals.

    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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