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seayniclabs

Mooring

Official
by seayniclabs
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Python
Local

Server Quality Checklist

58%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v0.1.1

  • Disambiguation4/5

    Clear separation between GitHub API (gh_*) and local git (repo_*) operations. While gh_issues combines list/create/update into one tool whereas PR operations are split into separate tools, the descriptions clearly distinguish their purposes with no overlapping functionality between different tools.

    Naming Consistency3/5

    Uses consistent prefixes (gh_ and repo_) but mixes naming patterns: gh_issues and gh_actions use nouns while PR tools use verb_noun (gh_pr_create, gh_pr_list). Local git tools follow git command names (mostly nouns) but lack verb consistency with the GitHub action-oriented names.

    Tool Count5/5

    Twelve tools is well-scoped for a server bridging GitHub API and local git operations. The count covers essential inspection and management tasks without bloat, with each tool earning its place in the set.

    Completeness3/5

    Strong read-only coverage (status, log, diff, blame, list operations) but notable gaps in write operations: no PR update/merge/close, no local git commit/push/pull/checkout, and limited Actions support (list only). The surface supports inspection workflows well but leaves agents unable to complete full git/PR lifecycle operations.

  • Average 3.4/5 across 12 of 12 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 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.

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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