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sgasser

draft-mcp-server

by sgasser
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TypeScript
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Server Quality Checklist

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

  • Latest release: v0.1.0

  • Disambiguation5/5

    Only one tool exists, so no risk of confusion between tools.

    Naming Consistency5/5

    The single tool name 'review_draft' follows a clear verb_noun pattern, which is consistent within the set.

    Tool Count4/5

    The server has exactly one tool, which is minimal but appropriate for a focused drafting/review utility. While the count is low, the purpose is narrow enough that it doesn't feel insufficient.

    Completeness5/5

    The server fully delivers on its stated purpose: providing a review step for drafts before sending to other tools. No additional tools are needed for this specific function.

  • Average 4.7/5 across 1 of 1 tools scored.

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

  • This repository is archived. Archived repositories automatically receive an F maintenance tier.

  • 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.

  • Add a glama.json file to provide metadata about your server.

  • 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.

  • Add related servers to improve discoverability.

How to sync the server with GitHub?

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.

Tool Scores

  • Behavior4/5

    Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

    Annotations indicate readOnlyHint=true, destructiveHint=false, idempotentHint=true. The description adds crucial behavioral details: 'Opens a browser window with an editor and live preview. Blocks until the user approves or rejects.' This discloses the interactive nature and UI behavior. It also explains the consequences of approval/rejection, which goes beyond annotations.

    Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

    Conciseness5/5

    Is the description appropriately sized, front-loaded, and free of redundancy?

    The description is well-structured with a concise opening sentence, bullet points for outcomes, and clear examples. Every sentence adds value, and the length is appropriate for the complexity of the tool. It is front-loaded with the essential purpose.

    Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

    Completeness5/5

    Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

    Given the interactive nature of the tool (no output schema), the description thoroughly covers the behavior: opening an editor, blocking for approval/rejection, and next steps. It also mentions clipboard fallback. The description is complete for the tool's complexity and context signals.

    Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

    Parameters4/5

    Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

    Schema coverage is 100%, so the input schema already describes all parameters. The description adds value by providing examples showing how to use the parameters (e.g., app='github', mcp='github:create_pull_request') and context for 'format' and 'app'. This goes beyond the schema's descriptions, enhancing parameter understanding.

    Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

    Purpose5/5

    Does the description clearly state what the tool does and how it differs from similar tools?

    The description clearly states the tool's purpose: 'Let the user review and edit a draft before it goes anywhere.' It specifies the action (review and edit) and the resource (draft). There are no sibling tools to differentiate, but the description is specific and includes examples that reinforce the purpose.

    Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

    Usage Guidelines5/5

    Does the description explain when to use this tool, when not to, or what alternatives exist?

    The description explicitly states when to use the tool: 'When creating PR descriptions, Linear tickets, Slack messages, or emails: call this tool first, then use the approved content with the target tool.' It also explains the flow (blocks until approve/reject) and what to do on each outcome, providing clear usage guidance.

    Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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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