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Attilio81

MCP Public APIs

by Attilio81
OverviewSchemaRelated ServersScoreDiscussions
Python
Remote

Server Quality Checklist

50%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v0.1.0

  • Disambiguation5/5

    The two tools have completely distinct purposes: one lists existing data (list_categories) and one updates the underlying data source (update_index). There is no overlap in functionality or ambiguity about when to use each tool.

    Naming Consistency5/5

    Both tools follow a consistent verb_noun naming pattern (list_categories, update_index) with clear, descriptive names that indicate their actions and targets. The naming style is uniform throughout.

    Tool Count2/5

    With only 2 tools, the server feels severely under-scoped for a 'Public APIs' domain. This minimal set lacks essential operations like searching APIs, getting API details, or filtering categories, which are core expectations for such a service.

    Completeness2/5

    The tool surface is significantly incomplete for a public APIs index. While it covers index maintenance (update_index) and basic listing (list_categories), it misses fundamental operations like retrieving specific API information, searching APIs by criteria, or accessing API endpoints, leaving agents unable to perform typical API discovery tasks.

  • 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
    • 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

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  • 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.

GitHub Badge

Glama performs regular codebase and documentation scans to:

  • Confirm that the MCP server is working as expected.
  • Confirm that there are no obvious security issues.
  • Evaluate tool definition quality.

Our badge communicates server capabilities, safety, and installation instructions.

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