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Server Quality Checklist

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  • Latest release: v1.2.0

  • Disambiguation5/5

    With only one tool, there is no possibility of confusion between tools. The tool's purpose is clearly defined as the sole interface for code operations.

    Naming Consistency5/5

    The single tool named 'write' follows a simple verb convention. With only one tool, there are no inconsistencies to assess.

    Tool Count2/5

    A server dedicated to code operations with only one tool is too few. The scope of the server suggests the need for additional tools like reading, searching, or deleting code, making this count insufficient.

    Completeness2/5

    The tool only handles writing and modifying files. It lacks essential operations like reading, deleting, or listing code, resulting in significant gaps for a code-focused server.

  • Average 4/5 across 1 of 1 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 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.

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    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.

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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.

Tool Scores

  • Behavior4/5

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

    With no annotations provided, the description must carry full behavioral transparency. It discloses that it creates new files automatically, modifies with smart diffs, shows emoji-indicated diffs, supports context_files, and handles all languages with error handling. This is substantial, though it omits details like overwrite behavior or output format, so it's not a perfect 5.

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

    Conciseness2/5

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

    The description is overly verbose and repetitive. It uses urgent all-caps ('MANDATORY', 'ONLY interface'), multiple emoji sections, and repeats the same ideas (e.g., 'code generation' appears in features and use cases). Several sentences add little value, such as 'Handles all programming languages' and the final reminder. This could be cut to a few concise sentences.

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

    Completeness3/5

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

    The tool has 3 parameters, no output schema, and no annotations. The description covers features, use cases, and error handling, so it is fairly complete for understanding what the tool does. However, it never explains what the tool returns or what the result of a call looks like (e.g., success message, applied diff), which is a notable gap given the absence of an output schema.

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

    Parameters3/5

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

    Schema description coverage is 100%, so baseline is 3. The description adds marginal value by mentioning that context_files are for 'better code understanding' and that file_path + prompt should be used together, but it mostly reiterates what's already in the schema. No significant new semantic information beyond the schema is provided.

    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 that this tool is for code generation, file creation, and modifications, with a specific verb and resource. It also explicitly positions itself as the only interface for code operations, leaving no ambiguity about its 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 gives explicit when-to-use guidance: 'USE THIS FOR ALL CODE OPERATIONS' and lists concrete use cases (writing new code, editing, code generation). It also provides a when-not-to-use instruction: 'Never edit files directly!' This is strong usage direction even though no sibling tools exist.

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

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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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