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Baneado98

secret-scanner

Server Quality Checklist

75%
Profile completionA complete profile improves this server's visibility in search results.
  • Latest release: v0.1.1

  • Disambiguation5/5

    Only one tool exists, so there is no risk of confusion or overlap between tools.

    Naming Consistency5/5

    With a single tool, naming is trivially consistent; the name 'scan_for_secrets' clearly describes its action.

    Tool Count4/5

    A single tool for a focused purpose like secret scanning is reasonable, though additional tools for repository scanning or configuration could be expected for broader coverage.

    Completeness4/5

    The tool comprehensively scans various secret types and provides detailed findings, but lacks capabilities like scanning entire repositories or managing ignore lists, which are minor gaps.

  • Average 4.3/5 across 1 of 1 tools scored.

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

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

  • Add a glama.json file to provide metadata about your server.

  • 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

  • Behavior5/5

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

    No annotations are provided, so the description carries the full burden. It fully discloses the scan is local and secret never transmitted. It details output components: verdict, secret type, provider, severity, line:column, masked excerpt, remediation note. This is comprehensive and honest.

    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 paragraph of 5 sentences, efficiently front-loaded with the main action and use cases. Each sentence adds value: examples, output description, usage instruction, privacy note. Slightly lengthy but not wasteful.

    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, no output schema, and no annotations, the description covers purpose, detected secrets, output details, privacy, and usage. It lacks explanation of the 'deep' parameter's behavior, which is only in schema. Otherwise, it is sufficiently complete.

    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 has 100% description coverage, so baseline is 3. The tool description does not add extra meaning to parameters beyond the schema; it focuses on overall behavior. Thus, no additional value for parameter semantics.

    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 scans code/text/diff for leaked secrets and lists many detectable types. It specifies use cases before commits, pushes, PRs, or pasting. This is specific, verb+resource-driven, and distinguishes it from any general text scanner.

    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?

    Explicitly tells when to use: before commit, push, PR, or pasting. The instruction 'Use this on every diff/file you are about to share' is clear. No explicit when-not-to-use or alternatives are mentioned, but the given context is sufficient.

    Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

GitHub Badge

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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