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Server Quality Checklist

58%
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  • Latest release: v1.0.0

  • Disambiguation5/5

    With only one tool, there is no possibility of ambiguity or overlap between tools, as there are no other tools to confuse it with. The single tool's purpose is clearly distinct by default in this minimal set.

    Naming Consistency5/5

    The tool name 'sample-tool' uses a consistent hyphenated noun pattern, and with only one tool, there is no inconsistency to evaluate. The naming is straightforward and follows a simple convention.

    Tool Count2/5

    A single tool is too few for most practical server purposes, as it severely limits functionality and scope. This feels thin and inadequate for handling any meaningful domain or workflow beyond basic demonstration.

    Completeness1/5

    The server is severely incomplete, with only a sample tool that lacks any clear domain or operational coverage. There are obvious gaps, as no CRUD, lifecycle, or specific functionality is provided, making it impossible for agents to perform useful tasks.

  • Average 2.3/5 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.

  • 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

  • Behavior1/5

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

    The description provides zero behavioral information beyond the name. With no annotations provided, the description carries the full burden of disclosing behavioral traits like whether this is a read or write operation, what side effects it might have, authentication requirements, or rate limits. The description fails to address any of these aspects, leaving the agent completely in the dark about how this tool behaves.

    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 extremely concise at just one sentence with no wasted words. It's appropriately sized for what little information it conveys, and while it's under-specified, it's not verbose or poorly structured. Every word in 'A sample tool for demonstration purposes' serves its purpose within the minimal context provided.

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

    Completeness2/5

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

    Given that this is a tool with one parameter, no annotations, no output schema, and no sibling tools, the description is incomplete. It fails to explain what the tool actually does, what behavior to expect, or what context it operates in. While the simplicity of the tool might lower expectations, the description doesn't provide enough information for an agent to understand when and how to use it effectively.

    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?

    With 100% schema description coverage and only one parameter documented in the schema, the description adds no additional parameter information. The schema already describes the 'input' parameter as 'Input parameter for the sample tool,' so the description doesn't compensate or add meaning beyond what's in the structured data. This meets the baseline of 3 when schema coverage is high.

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

    Purpose2/5

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

    The description 'A sample tool for demonstration purposes' is a tautology that essentially restates the tool name 'sample-tool' without specifying what it actually does. It doesn't mention any specific verb or resource, nor does it explain what kind of demonstration it performs. While it's not misleading, it provides minimal functional information beyond the name itself.

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

    Usage Guidelines2/5

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

    The description provides no guidance on when to use this tool, what context it's appropriate for, or what alternatives might exist. With no sibling tools mentioned, there's no need for differentiation, but the description fails to establish any usage context whatsoever. It doesn't indicate whether this is for testing, learning, or any specific scenario.

    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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