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easakura

Japan Parliament Search MCP

by easakura
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TypeScript
Remote

Server Quality Checklist

67%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v0.1.0

  • Disambiguation5/5

    The two tools have clearly distinct purposes: search_meetings focuses on meeting-level search to understand which committee discussed what, while search_speeches provides full-text speech search. There is no overlap in functionality.

    Naming Consistency5/5

    Both tools follow a consistent 'verb_noun' pattern with 'search_' prefix plus a plural noun (meetings, speeches), making the naming predictable and clear.

    Tool Count4/5

    Only 2 tools is slightly below the typical 3-15 range, but for a focused task like searching Japanese parliament records, this minimal set can be sufficient if it covers the core use cases of meeting discovery and speech retrieval.

    Completeness3/5

    The tools cover primary search needs but lack additional capabilities like retrieving individual meeting details, filtering by exact dates, or exporting results. Minor gaps that agents can work around with careful queries.

  • Average 4/5 across 2 of 2 tools scored.

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

    • No community issues in the last 6 months
    • 4 commits in the last 12 weeks
    • No stable releases found
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI status not available

  • 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

  • Behavior3/5

    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 discloses default behavior (returns 400-character excerpt) and the full_text parameter effect, but does not mention rate limits, authentication needs, pagination, or any destructive aspects. It covers basic behavior but lacks depth.

    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 very concise: four sentences in Japanese, totaling about 150 characters. It front-loads the core action and immediately provides context. Every sentence adds meaningful information without redundancy.

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

    Completeness3/5

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

    Given 8 parameters (one required) and no output schema, the description adequately explains the default behavior (excerpt vs. full text) and filtering options. However, it does not specify the output format (e.g., list of objects, sorting), pagination details, or how to handle empty results, leaving gaps for an AI agent.

    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 coverage is 100%, so baseline is 3. The description adds minimal extra meaning beyond the schema's parameter descriptions; it repeats the default excerpt length and full_text behavior, but does not provide significantly new semantic context for parameters like 'from', 'until', or 'speaker'.

    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 tool's purpose: full-text search of Japanese National Diet minutes from 1947 to latest, with specific filtering options (speaker, house, meeting, date range). It distinguishes itself from the sibling tool 'search_meetings' by focusing on speeches rather than meetings, even if not explicitly stated.

    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 description lists use cases (policy research, media, lobbying, compliance) but does not provide explicit guidance on when NOT to use the tool or when to prefer the sibling tool 'search_meetings' instead. Usage context is implied but not clearly delineated.

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

  • Behavior3/5

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

    No annotations are provided, so the description carries full burden. It states the tool searches meetings by keyword but does not disclose behavioral traits such as sorting, pagination, authentication needs, or the exact structure of returned data. The max_results parameter is documented, offering minimal behavioral context.

    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 very concise: two sentences with no wasted words. The first sentence states the core function, the second provides usage context and redirects to sibling. All information is front-loaded.

    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?

    Given the absence of an output schema, the description does not explain the return format or fields. However, the tool is a search operation with well-documented parameters (100% coverage) and a clear purpose. The mention of the sibling tool adds completeness. A brief note on output structure would improve it, but it is mostly adequate.

    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 coverage is 100%, so the description adds limited extra meaning beyond the parameter descriptions. It mentions 'keyword' which is already in the query parameter description. No significant additional semantics.

    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 tool searches for Diet meetings (plenary and committee) by keyword. It uses specific verb 'search' and resource 'meetings', and explicitly distinguishes from the sibling tool search_speeches, which handles individual speech content.

    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 explains when to use this tool (to get an overview of which committee discussed what and when) and explicitly directs the agent to search_speeches for individual speech content, providing a clear alternative.

    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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  • Confirm that there are no obvious security issues.
  • Evaluate tool definition quality.

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