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oaslananka

MCP Infra Lens

by oaslananka
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TypeScript
Hybrid

Server Quality Checklist

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

  • Latest release: v1.0.1

  • Disambiguation4/5

    Tools are mostly distinct, though analyze_server and compare_to_baseline both perform explanatory analysis of deviations, which could cause brief hesitation. snapshot vs analyze_server clearly separates raw collection from analysis.

    Naming Consistency4/5

    Four tools follow a clear verb_noun pattern (analyze_server, get_history, record_baseline, compare_to_baseline). snapshot stands alone without a verb prefix, though it functions as a verb in context—minor deviation but readable.

    Tool Count5/5

    Five tools is ideal for this focused scope: baseline lifecycle (record, compare), historical retrieval, real-time analysis, and raw capture. Each serves a specific step in the monitoring workflow without bloat.

    Completeness4/5

    Covers the core monitoring lifecycle: establishing baselines, recording current state, historical lookup, and comparative analysis. Minor gaps exist (no list_baselines or delete_baseline), but agents can work around these.

  • Average 3.2/5 across 4 of 5 tools scored.

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

    • No community issues in the last 6 months
    • No commit activity data available
    • No stable releases found
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI is failing

  • 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.

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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.

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