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Server Quality Checklist

58%
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  • Latest release: v2.0.0

  • Disambiguation5/5

    With only one tool, there is no ambiguity between tools. The tool's description clearly states its purpose, making selection straightforward.

    Naming Consistency5/5

    The single tool name 'biel_ai' is clear and consistent with the server name. No pattern inconsistencies are possible with only one tool.

    Tool Count3/5

    A single tool is on the low end, feeling thin for a server that might expect to offer more capabilities. It is not trivial, but the number is borderline and may be insufficient for broader use cases.

    Completeness4/5

    The tool covers the basic need of querying the specialized AI. However, there may be missing operations such as managing sessions or retrieving context, which could be expected in a full-featured AI interface.

  • Average 2.9/5 across 1 of 1 tools scored.

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

    • 0 of 1 community issues answered or closed in the last 6 months
    • 10 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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    {
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      "maintainers": [
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      ]
    }

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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

  • Behavior2/5

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

    With no annotations provided, the description carries the full burden of disclosing behavioral traits. It only says 'Query,' which implies a read-like operation, but does not disclose that this may send data to an external AI service, require authentication (though params suggest an API key), or have any side effects. Important behavioral context such as network calls, data privacy, or response handling is omitted.

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

    Conciseness4/5

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

    The description is a single, focused sentence that front-loads the action and purpose without any filler. It is concise and well-structured, though it could potentially add a hint about response format or usage context without becoming verbose. Still, it earns its place and is not over-specified.

    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?

    While the input schema comprehensively documents all parameters, the description lacks guidance on what the AI returns (no output schema) and does not explain the broader context of how the tool should be invoked (e.g., optional domain, API key, chat continuation). For a query tool with 5 parameters, this is adequately functional but not rich enough to fully guide an agent without further clues.

    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?

    The schema description coverage is 100%, with all five parameters individually described in the input schema. The tool description itself adds no parameter-specific semantics beyond what the schema already provides. As a result, a baseline score of 3 is appropriate; the schema does the heavy lifting, and the description does not enhance understanding of parameters.

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

    Purpose4/5

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

    The description clearly states the tool's function: querying Biel.ai's specialized AI about code, SDKs, and documentation. It uses a specific verb ('Query') and resource ('Biel.ai's specialized AI'), making the purpose distinct even without sibling tools to differentiate from. However, the term 'specialized AI' is somewhat generic, which prevents a perfect score.

    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 versus alternatives, and no exclusion criteria. Since there are no sibling tools or context signals, the agent is left without explicit usage conditions or scenarios where this tool should or shouldn't be used. This is a clear gap in usage guidance.

    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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  • 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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