Skip to main content
Glama
Abdul4code

CodePecker

by Abdul4code
OverviewSchemaRelated ServersScoreDiscussions
Python
Local

Server Quality Checklist

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

  • Latest release: v0.1.0

  • Disambiguation5/5

    With only one tool, there is no possibility of selecting the wrong tool. The tool's purpose is clearly defined, covering review and remediation in a single action.

    Naming Consistency5/5

    The single tool name 'review_and_remediate' follows a clear verb_noun pattern, and consistency is trivially maintained with only one tool.

    Tool Count3/5

    The server has just one tool, which is below the typical 3-15 range. However, the tool is comprehensive, encapsulating review, remediation, verification, and reporting, so the thin count is acceptable for a focused purpose.

    Completeness4/5

    The tool covers the full lifecycle from code review to remediation to test verification, and returns a detailed scorecard and diff. Minor gaps include the inability to separate review-only from remediation, but an environment variable supports detect-only mode, so core workflows are covered.

  • Average 4.8/5 across 1 of 1 tools scored.

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

    • No community issues in the last 6 months
    • 27 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

  • Behavior5/5

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

    With no annotations, the description fully carries the transparency burden. It discloses automatic fixing, test execution, sandbox writing of support files, the non-execution of tests_dir tests, and environment-variable-driven mode changes. No contradictions detected.

    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 long but appropriately so for a complex tool. The opening paragraph front-loads the overall pipeline, and the Args section uses a clear bulleted structure. Every sentence adds behavioral or parameter value without repeating schema defaults.

    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?

    For a high-complexity tool, the description covers the full workflow, return artifact categories, mode-specific behavior, and sandbox limitations. The output schema handles return-type details, while the description supplies usage context and edge-case guidance, making it complete enough for an agent to invoke correctly.

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

    Parameters5/5

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

    Schema description coverage is 0%, but the description compensates thoroughly. Each parameter gets practical context: language default, test import convention (as `solution`), tests_dir RDY-03 attestation, support_files usage with an example, and the 'local files only' constraint. This is far more than the schema alone 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 states a specific, multi-step purpose: review code across security, standards, readiness, and sustainability; automatically fix issues; verify via tests; and return findings, scorecard, remediated code, diff, citations, and metrics. This goes well beyond a vague verb+noun and clearly delineates the tool's end-to-end scope.

    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 gives clear context on when to supply optional inputs (tests, tests_dir, support_files) and how detect-only mode (CODEPECKER_REMEDIATE=false) changes behavior. Since there are no sibling tools, explicit alternatives are unnecessary, but it stops short of explicitly stating when not to use the tool.

    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

CodePecker MCP server

Copy to your README.md:

Score Badge

CodePecker 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/Abdul4code/CodePecker'

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