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minovap

openrouter-mcp

by minovap
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JavaScript
Hybrid

Server Quality Checklist

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

  • Latest release: v2.0.1

  • Disambiguation5/5

    With only one tool, there is no risk of confusion between tools. The single tool is clearly distinct by default.

    Naming Consistency5/5

    The tool name 'ask_model' follows a clear verb_noun convention, which is appropriate and consistent though there is only one tool.

    Tool Count3/5

    A single tool feels thin for a server named 'openrouter-mcp', which implies broader model access capabilities. However, it may be scoped to just querying models, making it borderline acceptable.

    Completeness3/5

    The tool covers the core action of asking a model, but lacks supporting tools like listing available models or retrieving model metadata, which are notable gaps for a server focused on model access.

  • Average 3.8/5 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.

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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?

    No annotations are provided, so the description carries full burden for behavioral disclosure. It fails to mention behavioral traits such as latency, context retention, side effects, or required permissions, which are important for an AI agent to correctly invoke the tool.

    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 two sentences, with the first sentence stating the purpose and the second providing usage guidance. It is front-loaded and contains no superfluous information.

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

    Completeness4/5

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

    Given the tool's complexity (4 parameters, no output schema, no nested objects), the description adequately covers purpose and usage. However, it lacks behavioral details that would help an agent understand what happens when the tool is invoked.

    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 the baseline is 3. The description does not add significant meaning beyond the schema; it only provides high-level context about use cases. No additional parameter details are given.

    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 consults another AI model for coding tasks, using the specific verb 'consult' and identifying the resource as 'another AI model'. With no sibling tools provided, differentiation is not required.

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

    Usage Guidelines4/5

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

    The description explicitly lists use cases: 'get a second opinion, ask for explanations, or request assistance with complex problems'. However, it does not provide when-not-to-use or alternatives, but the absence of sibling tools reduces the need.

    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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