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RufengLai

ANSA API MCP Server

by RufengLai
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Python
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Server Quality Checklist

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

  • Latest release: v0.1.0

  • Disambiguation5/5

    With only one tool, there is no possibility of confusion or overlap. The tool has a clear and singular purpose of searching the ANSA API documentation.

    Naming Consistency5/5

    The single tool follows a consistent verb_noun pattern ('search_ansa_api'), which is clear and predictable.

    Tool Count4/5

    One tool is appropriate for a focused documentation search server. While minimal, it aligns with the server's narrow scope.

    Completeness4/5

    The tool provides comprehensive search functionality with query, module, category filters, and result limiting. Minor gaps like listing available modules or categories exist but are not critical for the core search task.

  • Average 3.9/5 across 1 of 1 tools scored.

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

    • No community issues in the last 6 months
    • 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 status not available

  • This repository is licensed under MIT License.

  • This repository includes a README.md file.

  • No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.

    Tip: use the "Try in Browser" feature on the server page to seed initial usage.

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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 the burden. It mentions keyword language support but does not disclose non-obvious behaviors like rate limits, pagination (only top_n count), or result format. Moderate transparency.

    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 structured with an Args section and is reasonably concise. However, it could be slightly shortened by removing the default value repetition for top_n (already in schema) without losing clarity.

    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 parameter semantics are well-covered, but the description omits what the search returns (e.g., list of documentation sections, relevance scores). An output schema is present but not seen; assuming it covers return structure, the description still lacks context on search scope (e.g., full text vs title only).

    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%, so the description fully compensates. Each parameter is explained with examples (e.g., module: 'ansa.mesh', category: 'mesh_edit'), and top_n includes a default value. This adds significant meaning beyond the schema's type/title-only definitions.

    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 'Search the ANSA Python API documentation' with a specific verb and resource, and includes details about keyword support. Although no sibling tools exist for differentiation, the purpose is 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?

    No explicit guidance on when or when not to use this tool. With no sibling tools, the lack of alternatives context is less critical, but the description does not mention any prerequisites or ideal use cases, resulting in a neutral score.

    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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