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duskwire

preflight402

by duskwire
OverviewSchemaRelated ServersScoreDiscussions
Python
Hybrid

Server Quality Checklist

75%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v0.1.0

  • Disambiguation5/5

    With only one tool available, there is no possibility of confusion between tools. The tool's purpose is clearly defined as a URL preflight check.

    Naming Consistency5/5

    The single tool name 'preflight' is clear and appropriate for its function, and with only one tool, there is no inconsistency to evaluate.

    Tool Count3/5

    A single tool is on the low end of the scale, suggesting a thin server. However, the tool is comprehensive and well-scoped to the server's stated purpose of preflight checks, making it borderline.

    Completeness5/5

    The preflight tool covers all essential aspects of the intended function: probing URLs, checking liveness/TLS, payment handshake, network detection, identity, and recommendation. No obvious gaps exist within the preflight domain.

  • Average 4.4/5 across 1 of 1 tools scored.

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

    • No community issues in the last 6 months
    • 56 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.

  • This repository includes a glama.json configuration file.

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    {
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}

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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

  • Behavior5/5

    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 behavior. It explicitly states the call is free, requires no credentials, and returns a detailed verdict, including the recommendation and the fact that paid-tier fields are null. This is thorough and transparent about the tool's non-invasive, read-only nature.

    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 well-structured, starting with a benefit statement and then enumerating the key return fields and limitations. It's slightly dense but every sentence adds value, and it avoids unnecessary fluff. It could be more concise, but the structure is effective.

    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 that there is an output schema (not shown here) and no annotations, the description is remarkably complete. It explains the purpose, the exact checks performed, the returned recommendation, and the free-tier limitations. This provides sufficient context for an agent to decide when and how to invoke the tool, and what to expect in return.

    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 input schema already provides 100% coverage for the single 'url' parameter with a clear description. The tool description does not add new semantic information about the parameter itself, but it does contextualize its use (probes the URL). This aligns with the baseline of 3 for full schema coverage.

    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 a specific action: 'Probes the URL' and returns a 'trust-preview.v1 verdict' with detailed criteria. It distinguishes itself as a preflight check before payment, which is a unique and well-defined purpose.

    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 opens with 'One free call before your agent pays,' which clearly indicates when to use the tool. It also notes that no wallet, payment, or auth is involved, implying it's safe for initial checks. However, it does not explicitly mention when not to use it or provide alternatives, since 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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