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nohosa001-pixel

security-gate-x402

Server Quality Checklist

83%
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  • Latest release: v1.0.2

  • Disambiguation5/5

    With only one tool, there is no ambiguity or overlap. The tool's purpose is clearly defined and distinct.

    Naming Consistency5/5

    The tool name 'inspect_agent_output' follows a clear verb_noun pattern, which is consistent and descriptive. Since there is only one tool, naming is trivially coherent.

    Tool Count3/5

    A single tool feels thin for a 'security-gate' server, which might imply a broader set of operations. However, the one tool is comprehensive in scope, making the count borderline rather than severely inadequate.

    Completeness3/5

    The tool covers the core workflow of inspecting output and issuing attestation, but lacks verification or management support that would make it a fully complete security gate. These gaps are workable but notable.

  • Average 4.2/5 across 1 of 1 tools scored.

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

    • No community issues in the last 6 months
    • 11 commits in the last 12 weeks
    • No stable releases found
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI is passing
  • 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.

  • This server has been verified by its author.

  • Add related servers to improve discoverability.

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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?

    No annotations are provided, so the description carries the full behavioral burden. It discloses that the tool inspects and issues a cryptographic attestation, implying a read-only, verification-oriented behavior, and it names specific checks including dangerous AST executions. It does not detail every side effect or limitation, but the key behavioral outcome is stated clearly.

    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 three sentences with no redundancy: the first states scope and checks, the second states the output, the third notes the free trial. It is compact, front-loaded with the core purpose, and every sentence contributes useful 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?

    For a tool with no output schema and no annotations, the description covers the main purpose, the types of hazards detected, and the attestation output. It could be slightly more explicit about what the caller receives beyond the attestation and about edge cases such as missing ground truth, but overall it is complete enough for an agent to understand and invoke the tool correctly.

    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 input schema already documents all three parameters with descriptions and defaults. The description reinforces the relationship between 'context_ground_truth' and hallucination checking, but it does not add substantially new parameter-level semantics beyond what the schema already provides.

    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 opens with a specific verb, 'Inspects', names the exact resource ('an AI agent's text or code output'), and enumerates the concrete categories it checks for: prompt injections, secret leaks, dangerous AST executions, and hallucinations against ground truth. It also states the final output (EIP-191 Proof-of-Safety attestation), making the tool's purpose unambiguous even without sibling tools to compare against.

    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?

    There is no explicit 'use this when...' statement or alternative sibling, but the description clearly signals the intended use case: verifying the safety and factual accuracy of AI agent output. It gives enough context for an agent to infer when this tool is appropriate, though it does not state exclusions or prerequisites such as when a ground-truth comparison is impossible.

    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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