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sakthivelchan89

scan_your_ai_toolkit

by sakthivelchan89
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Server Quality Checklist

92%
Profile completionA complete profile improves this server's visibility in search results.
  • Disambiguation5/5

    With only one tool, there is no possibility of confusion or overlap with other operations. The single tool has a clearly defined purpose distinct from any hypothetical alternatives.

    Naming Consistency5/5

    The single tool follows a clear verb_noun pattern (probe_scan) using snake_case. With only one tool, there are no deviations or inconsistencies to evaluate.

    Tool Count2/5

    The server name 'scan_your_ai_toolkit' suggests a collection of scanning utilities, but having only one monolithic scan tool is too few for the apparent scope. A single tool cannot constitute a 'toolkit' for this domain.

    Completeness3/5

    While the tool covers the basic scan operation, notable operations are missing for a comprehensive scanning toolkit, such as configuring scan parameters, filtering specific target types, or retrieving/managing historical scan results.

  • Average 3.1/5 across 1 of 1 tools scored.

    See the tool scores section below for per-tool breakdowns.

  • This repository includes a README.md file.

  • Add a LICENSE file by following GitHub's guide.

    MCP servers without a LICENSE cannot be installed.

  • Latest release: v0.1.4

  • Tools from this server were used 7 times in the last 30 days.

  • This repository includes a glama.json configuration file.

  • View server inspector

  • This server provides 1 tool. View schema

  • No known security issues or vulnerabilities reported.

    Report a security issue

  • This server has been verified by its author.

  • View related servers

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.

Our badge communicates server capabilities, safety, and installation instructions.

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How to claim the server?

If you are the author of the server, you simply need to authenticate using GitHub.

However, if the MCP server belongs to an organization, you need to first add glama.json to the root of your repository.

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

Then, authenticate using GitHub.

Browse examples.

How to make a release?

A "release" on Glama is not the same as a GitHub release. To create a Glama release:

  1. Claim the server if you haven't already.
  2. Go to the Dockerfile admin page, configure the build spec, and click Deploy.
  3. Once the build test succeeds, click Make Release, enter a version, and publish.

This process allows Glama to run security checks on your server and enables users to deploy it.

How to add a LICENSE?

Please follow the instructions in the GitHub documentation.

Once GitHub recognizes the license, the system will automatically detect it within a few hours.

If the license does not appear on the server after some time, you can manually trigger a new scan using the MCP server admin interface.

How to sync the server with GitHub?

Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.

To manually sync the server, click the "Sync Server" button in the MCP server admin interface.

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.

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