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surajfale

Git Commit MCP Server

by surajfale
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Python
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Server Quality Checklist

67%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v0.1.2

  • Disambiguation5/5

    The two tools have clearly distinct purposes: one performs the full commit and push workflow, while the other only generates a commit message. There is no overlap or ambiguity.

    Naming Consistency5/5

    Both tool names follow the same snake_case convention and use a verb_noun pattern. 'git_commit_and_push' and 'generate_commit_message' are consistent in style.

    Tool Count3/5

    With only two tools, the server feels minimal but is scoped to git commit operations. It is borderline acceptable; more tools could be added for a richer experience.

    Completeness3/5

    The server covers commit and push, and commit message generation, but is missing other common git operations like staging/unstaging, viewing status, or undoing commits. Some gaps exist for a complete commit workflow.

  • Average 4.4/5 across 2 of 2 tools scored. Lowest: 3.9/5.

    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

  • 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.

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  "maintainers": [
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  ]
}

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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?

    With no annotations, the description discloses key behaviors: AI model (gpt-4o-mini), configurability via ENABLE_AI, fallback to heuristic, SSH-only constraint, and return format. It does not mention any destructive actions, which is appropriate. The behavioral coverage is good.

    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 concise (about 50 words), well-structured with bullet points, and front-loads the main action. Every sentence adds value, and the return format is clearly outlined.

    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 simplicity (one parameter, no annotations, has output schema) and the described behaviors (AI fallback, SSH constraint), the description is nearly complete. It could explicitly mention the repository_path parameter, but the default '.'' and the context of analyzing changes mitigates this gap.

    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?

    The input schema has 0% parameter description coverage, and the tool description does not mention the 'repository_path' parameter at all. The agent must infer its purpose (the path to the git repository) from context, which is insufficient for a single-parameter tool.

    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 verb ('Analyze changes and generate') and the resource ('a Conventional Commit message'). It distinguishes itself from the sibling tool 'git_commit_and_push' by focusing solely on message generation, implying no commit/push action.

    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 when to use the tool (to generate a commit message) but does not explicitly contrast with the sibling tool or state when not to use it. It does mention the SSH URL constraint for remote repos, which is helpful context.

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

  • Behavior5/5

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

    With no annotations provided, the description carries the full burden and effectively discloses all key behaviors: automated workflow, commit message generation, staging, committing, optional pushing, CHANGELOG.md update, and error handling. No hidden behaviors are omitted.

    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 well-structured with a concise summary, numbered steps, clear Args and Returns sections, and no redundant information. Every sentence adds value and is front-loaded.

    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?

    Given the tool's complexity (2 parameters, output schema, no annotations), the description is complete: it explains the workflow, parameters, return values, and behavior. The output schema details are provided in the description, and the context of the sibling tool is implicitly covered.

    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?

    The description provides detailed parameter semantics beyond the schema, especially for 'repository_path' with examples and supported formats. 'confirm_push' is clearly explained with its default value. Since schema had 0% description coverage, the description fully compensates.

    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 automates the Git commit workflow including tracking changes, generating commit message, committing, and optionally pushing. It distinguishes from the sibling tool 'generate_commit_message' by covering the full workflow.

    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 explains the tool's context and capabilities (local and remote repos, optional push) and implies when to use it, but does not explicitly state when not to use it or contrast with the sibling tool.

    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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