Skip to main content
Glama
ayushgundecha

codesentry-mcp

by ayushgundecha
OverviewSchemaRelated ServersScoreDiscussions
TypeScript
Local

Server Quality Checklist

58%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v1.0.0

  • Disambiguation5/5

    The two tools serve entirely different purposes: one checks connectivity and the other performs repository analysis. There is no overlap or ambiguity in their intended use.

    Naming Consistency4/5

    Both tool names are imperative verbs, but 'ping' is a bare verb while 'analyze_repository' follows a verb_noun pattern. This is a minor deviation from a fully uniform naming convention.

    Tool Count3/5

    With only two tools, the server feels minimal. For a repository analysis service, one might expect additional tools for managing or retrieving results, but the count is not unreasonable for a simple utility.

    Completeness3/5

    The core analysis functionality is present, but there are no supporting operations such as listing previous analyses, fetching detailed reports, or configuring analysis parameters. The surface is functional but incomplete for a comprehensive analysis workflow.

  • Average 3.6/5 across 2 of 2 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

  • Add a LICENSE file by following GitHub's guide. Once GitHub recognizes the license, the system will automatically detect it within a few hours.

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

    MCP servers without a LICENSE cannot be installed.

  • 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.

  • If you are the author, simply .

    If the server belongs to an organization, first add glama.json to the root of your repository:

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

    Then . Browse examples.

  • Add related servers to improve discoverability.

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.

Tool Scores

  • Behavior2/5

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

    The description provides no behavioral details such as whether the analysis is read-only, what permissions are needed, whether it has side effects, or what output is returned. With no annotations, the agent lacks critical information about the tool's runtime behavior, leaving a significant transparency gap.

    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 a single, front-loaded sentence that efficiently conveys the tool's purpose with no unnecessary words or redundancy. It is optimally concise and well-structured.

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

    Completeness2/5

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

    The tool has a nested config object with multiple toggles and no output schema, yet the description is minimal. It does not explain how to use the config parameter, what the tool returns, or any behavioral constraints. This is insufficient for an agent to invoke the tool correctly in varied scenarios.

    Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

    Parameters2/5

    Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

    Although the schema has 100% coverage, the description adds no explanation of the 'config' parameter or its options. The description lists security, performance, and quality, but the schema also includes 'enableDocumentation', which is not mentioned, creating a mismatch. The description adds little beyond the schema's property names and types.

    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 analyzes a git repository for security, performance, and quality issues. It uses a specific verb and resource, and is unambiguous. The only sibling tool is 'ping', which serves a different purpose, so there is no confusion.

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

    Usage Guidelines3/5

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

    The description implies usage context by stating what the tool does, but it does not explicitly say when to use it or provide alternatives. Since the only sibling is 'ping', the intended use is reasonably obvious, yet there is no formal guidance on when to choose this tool over others.

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

  • Behavior3/5

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

    No annotations are provided, so the description carries the transparency burden. It implies a safe, non-destructive connectivity check but does not disclose return values, error behavior, or side effects. For a simple ping, this is adequate but minimal.

    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?

    One sentence, front-loaded with the action, and zero waste. The description and schema together provide the necessary information without redundancy.

    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 simple connectivity test with one optional parameter, the description is sufficient. No output schema exists, so the return format is not explained, but a ping tool's purpose implies a simple success/failure response. Minor gap in not mentioning the echo behavior explicitly.

    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 coverage is 100%: the 'message' parameter is fully described in the schema as 'Optional message to echo back'. The description adds no additional parameter meaning, so the baseline of 3 applies.

    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 'Test connectivity to the CodeSentry server' uses a specific verb ('test') and a clear resource ('CodeSentry server'). It fully distinguishes from the sibling tool 'analyze_repository', which is about analysis, not connectivity.

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

    Usage Guidelines3/5

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

    The usage is implied: use it to verify server availability. No explicit alternatives or exclusions are mentioned. With only one sibling, the purpose distinction is clear, but there is no direct comparison or when-not-to-use guidance.

    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.

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

Card Badge

codesentry-mcp MCP server

Copy to your README.md:

Score Badge

codesentry-mcp MCP server

Copy to your README.md:

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/ayushgundecha/codesentry-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server