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gui11aume

docdb-mcp

by gui11aume
OverviewSchemaRelated ServersScoreDiscussions
Python
Remote

Server Quality Checklist

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

  • Latest release: v0.1.1

  • Disambiguation5/5

    Only one tool exists, so there is no possibility of confusion or ambiguity between tools.

    Naming Consistency5/5

    The single tool name follows a clear verb_noun pattern (resolve_docdb_id) and is self-explanatory.

    Tool Count3/5

    A single tool is minimal but can be appropriate for a very focused purpose like patent ID resolution. However, it feels thin for any broader use case.

    Completeness3/5

    The server covers only ID resolution, missing related operations like search, listing, or batch processing. For a narrow purpose it may be sufficient, but agents have no fallback if resolution fails.

  • Average 4.9/5 across 1 of 1 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
    • Last stable release on
    • 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

  • Behavior5/5

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

    With no annotations, the description fully discloses behavior: it explains the output structure, the meaning of multiple records, error codes returned (not exceptions), and edge cases like leading zeros. It does not contradict any annotations (none provided).

    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 well-structured with sections (IMPORTANT, Args, Returns, troubleshooting) and front-loaded with critical instructions. It is somewhat lengthy but every sentence serves a purpose. Minor redundancy (e.g., list of country codes) could be trimmed but overall efficient.

    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 covers input preparation, output interpretation, error handling, and edge cases. It includes troubleshooting guidance and processing advice. There are no sibling tools, so no differentiation needed. It is complete for the given complexity and schema richness.

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

    Parameters5/5

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

    Schema description coverage is 0%, but the description thoroughly explains both parameters: cc (two-letter DOCDB country code with examples) and number (publication number without kind code, formatting, or special characters). It includes constraints and typical values.

    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: 'Resolve a patent publication number to its canonical DOCDB record(s).' It specifies the exact action (resolve) and the resource (patent publication number to DOCDB records). No ambiguity.

    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 provides explicit instructions on how to prepare inputs (strip kind code, format numbers), gives examples, troubleshooting steps for empty results, and advice on using context. Although no siblings are listed, the guidance is comprehensive and directly applicable.

    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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  • Evaluate tool definition quality.

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