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backloghq

backlog

by backloghq
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TypeScript

Server Quality Checklist

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

  • Latest release: v1.5.0

  • Disambiguation5/5

    Each tool has a distinct, clearly separated purpose with explicit cross-references in descriptions (e.g., 'use task_log instead', 'use task_doc_write instead'). No overlapping functionality—CRUD, lifecycle, archival, and document operations are cleanly partitioned.

    Naming Consistency5/5

    Strict snake_case convention with consistent 'task_' prefix. Sub-resources follow predictable patterns (task_doc_read/write/delete, task_archive_list/load). Verbs are clear and consistently placed (task_add, task_delete, task_modify).

    Tool Count4/5

    24 tools is above the typical ideal range but justified by the domain complexity. The set covers full task lifecycle, document attachments, archival management, annotations, and discovery without redundancy. Each tool earns its place for a comprehensive backlog system.

    Completeness4/5

    Excellent coverage of CRUD, status workflow (start/stop/done), soft-delete with purge, annotations, document attachments, and archival. Minor gap: archived tasks are view-only with no restore-to-active operation, though this appears to be an intentional cold-storage design.

  • Average 4.5/5 across 22 of 24 tools scored.

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

    • No community issues in the last 6 months
    • 1 commit in the last 12 weeks
    • No stable releases found
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI is passing

  • 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.

  • This repository includes a glama.json configuration file.

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    {
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}

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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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