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asgard-ai-platform

MCP Taiwan Judgment Search

Server Quality Checklist

67%
Profile completionA complete profile improves this server's visibility in search results.
  • Latest release: v0.3.0

  • Disambiguation5/5

    Each tool serves a distinct purpose: retrieving full judgment text, getting PDF links, looking up legal terms, and searching judgments. No functional overlap.

    Naming Consistency5/5

    All tool names follow a consistent verb_noun pattern in snake_case (get_judgment, get_judgment_pdf, lookup_legal_term, search_judgments), making them predictable.

    Tool Count5/5

    With 4 tools covering search, retrieval, PDF download, and legal term lookup, the count is well-scoped for a judgment search server—neither too few nor too many.

    Completeness5/5

    The tool surface covers the complete workflow: search for judgments, retrieve full text or PDF, and look up legal terms. No obvious gaps for the intended domain.

  • Average 3.8/5 across 4 of 4 tools scored.

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

    • 0 of 1 community issues answered or closed in the last 6 months
    • 0 commits in the last 12 weeks
    • Last stable release on
    • 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, and the description does not disclose behavioral traits such as read-only nature, pagination behavior beyond the 'page' parameter, sorting, or rate limits. This is a significant gap for a 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.

    Conciseness5/5

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

    The description is concise with three front-loaded sentences covering purpose and output format. No redundant information.

    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 the tool has no output schema, the description partially covers return fields but omits details like page size (though specified in param description), maximum results, ordering, or error handling. Adequate but not fully complete.

    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% with descriptions for both parameters. The tool description adds no extra semantics beyond the schema; it explains output fields but not parameter meaning. Baseline 3 is appropriate.

    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 full-text of court judgments and returns a list. It specifies output fields and links judgment_id to the sibling get_judgment, distinguishing from other tools.

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

    Usage Guidelines4/5

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

    The description implies usage for searching judgments and suggests get_judgment for full text, but lacks explicit when-to-use or when-not-to-use guidance. The sibling context helps, but direct instructions are missing.

    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?

    With no annotations, the description carries the full burden. It explains the return dict structure (always includes judgment_id and url; additionally path, size_bytes, cached when downloaded) and hints at conditional behavior via save_to parameter. However, it omits details on error handling, authentication, or expiration of URLs.

    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 extremely concise—two short sentences—and perfectly front-loaded with the core action. Every sentence adds value, with no redundancy or fluff.

    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 tool's simplicity, no output schema, and 100% param coverage, the description adequately covers return values and conditional download behavior. It is missing only minor details like error responses or concurrency guarantees, but is still reasonably complete.

    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%, so the baseline is 3. The description adds minimal parameter semantics beyond the schema, only noting that judgment_id comes from search_judgments and implicitly repeating save_to behavior. It does not significantly enhance understanding of the parameters.

    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 action ('取得裁判書 PDF 連結' - get judgment PDF link) and resource ('裁判書 PDF'). It also mentions the optional download capability, differentiating it from siblings that likely deal with text or legal terms.

    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 provides context that the 'judgment_id' can be obtained from 'search_judgments', implying a prerequisite. However, it does not explicitly state when to use this tool versus alternatives like 'get_judgment' or when not to use it, lacking clear usage boundaries.

    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 returns full content including case info and text, which implies a read-only retrieval operation. However, it does not disclose any behavioral traits such as idempotency, rate limits, or authentication requirements. The basic nature of a get tool allows a score of 3.

    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 part is essential: verb, resource, and output scope.

    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?

    The tool is simple (one parameter, no output schema, no nested objects). The description explains the return value as '包含案件資訊與全文' but does not detail structure or fields. Without an output schema, the description should provide more specificity, but the basic information is present.

    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% (one parameter with a clear description and format example). The tool description adds no additional parameter meaning beyond what the schema already provides. Per rubric, baseline is 3 for high 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 uses specific verb+resource: '取得單一裁判書的完整內容' (get full content of a single judgment). It clearly distinguishes from siblings: 'single' contrasts with search_judgments (multiple), and '完整內容' differs from get_judgment_pdf (PDF) and lookup_legal_term (term lookup).

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

    Usage Guidelines4/5

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

    The description implies usage when needing full content for a specific judgment. The schema parameter description explicitly states the judgment_id comes from search_judgments, providing clear context for when to use this tool. However, it does not mention when not to use or explicitly name alternatives.

    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?

    With no annotations, the description carries full burden. It implies a read operation ('查詢') and explains multiple domain results and filtering, but does not disclose error handling, authentication needs, or response format for missing terms.

    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 two sentences, front-loaded with purpose, and every sentence adds value. No wasted words.

    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 no output schema, the description could better explain the return structure (e.g., list of definitions per domain) and behavior for missing terms, but it covers essential functionality.

    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 new info beyond schema; the domain parameter's default behavior ('all domains returned') is already stated in the schema's description.

    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 queries the judicial dictionary for legal term definitions, specifying verb (查詢), resource (司法院裁判書用語辭典), and distinguishing it from sibling tools like get_judgment and search_judgments.

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

    Usage Guidelines4/5

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

    The description provides clear context for when to use this tool (to look up legal terms) and explains the domain filtering behavior, but does not explicitly state when not to use it or mention alternatives.

    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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