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Server Quality Checklist

67%
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  • Latest release: v0.1.0

  • Disambiguation4/5

    Tools are semantically distinct with clear resource targeting (card vs board vs column vs label) and action specificity (create vs duplicate vs graduate, add vs toggle). While the large set creates cognitive load, descriptive prefixes prevent overlap between similar operations like list_cards (filtered) and search_cards (keyword across all boards).

    Naming Consistency5/5

    Exemplary consistency across all 32 tools. Every name follows snake_case with standardized verb_object structure (e.g., create_card, add_labels_to_card, set_active_board, toggle_checklist_item). Verbs are uniform throughout the CRUD and lifecycle operations.

    Tool Count2/5

    32 tools significantly exceeds the threshold for effective agent selection (25+). The surface is overly granular, exposing fine-grained operations (separate tools for color, checklist items, single vs batch create) that could be consolidated into parameters, increasing selection error rates.

    Completeness4/5

    Strong coverage of the project management domain including full card lifecycle, board/column management, labeling, assignment, time tracking, and git integration. Minor gaps exist (no delete board, no update/delete comment), but core video production workflows (graduate_to_production, Idea Pool) are well supported.

  • Average 3.5/5 across 32 of 32 tools scored. Lowest: 2.7/5.

    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.

  • This repository includes a glama.json configuration file.

  • If you are the author, simply .

    If the server belongs to an organization, first add glama.json to the root of your repository:

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

    Then . Browse examples.

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Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.

To manually sync the server, click the "Sync Server" button in the MCP server admin interface.

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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framedeck-mcp MCP server

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