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LGTMCP

by shields
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Server Quality Checklist

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

  • Latest release: v0.0.0-4996cee

  • Disambiguation5/5

    The two tools have clearly distinct purposes: one reviews and commits if approved, while the other only reviews without committing. There is no overlap or ambiguity between them.

    Naming Consistency5/5

    Both tools follow a consistent verb-based naming pattern (review_and_commit, review_only), using snake_case throughout. The naming is predictable and aligned with their functions.

    Tool Count2/5

    With only 2 tools, the server feels too thin for a code review and commit domain, lacking operations like listing reviews, updating commits, or handling rejections. This minimal set limits agent workflows.

    Completeness2/5

    The server covers basic review and commit actions but has significant gaps: no tools for managing or querying existing reviews/commits, no update or delete operations, and no error handling for edge cases, making the surface incomplete for robust code management.

  • Average 3.8/5 across 2 of 2 tools scored.

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

    • No community issues in the last 6 months
    • 77 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 Apache 2.0.

  • 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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  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "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

  • Behavior3/5

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

    With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions that the tool uses Gemini for review and returns feedback and approval status, which adds some context beyond basic functionality. However, it lacks details on permissions, rate limits, error handling, or what 'approval status' entails, leaving gaps in behavioral understanding.

    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 concise and front-loaded, consisting of one sentence that efficiently conveys the core functionality. There's no wasted text, and it gets straight to the point. However, it could be slightly more structured by explicitly contrasting with the sibling tool for better clarity.

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

    Completeness3/5

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

    Given the tool's moderate complexity (code review with AI), no annotations, and no output schema, the description is somewhat complete but has gaps. It explains the purpose and outcome but lacks details on the review process, output format, or error scenarios. It's adequate as a minimum viable description but could be more comprehensive for better agent understanding.

    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?

    The input schema has 100% description coverage, with the single parameter 'directory' clearly documented. The description doesn't add any parameter-specific information beyond what the schema provides, such as format examples or constraints. According to the rules, with high schema coverage, the baseline is 3, and the description doesn't compensate with extra details.

    Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

    Purpose4/5

    Does the description clearly state what the tool does and how it differs from similar tools?

    The description clearly states the tool's purpose: 'Review code changes using Gemini and return feedback without committing.' It specifies the action (review), the method (using Gemini), and the outcome (return feedback without committing). However, it doesn't explicitly distinguish this from its sibling 'review_and_commit' beyond the 'without committing' phrase, which is implied but not directly contrasted.

    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 this tool through the phrase 'without committing,' suggesting it's for review-only scenarios. However, it doesn't explicitly state when to use this versus the sibling 'review_and_commit' or provide any alternatives or exclusions. The guidance is present but minimal and not comprehensive.

    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?

    With no annotations, the description carries full burden. It discloses key behavioral traits: uses Gemini for review, conditionally commits based on approval, and returns different outcomes (comments vs. success message with commit hash). However, it lacks details on review criteria, what 'approved' means, error handling, or side effects like branch changes.

    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 highly concise and front-loaded: a single sentence efficiently conveys the tool's core functionality, conditional logic, and return outcomes. Every word earns its place with zero waste or 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?

    Given no annotations and no output schema, the description does well by explaining the conditional behavior and return values. However, as a mutation tool (commits changes), it could benefit from more details on permissions, review standards, or error cases to be fully complete for agent use.

    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 description coverage is 100%, so the schema already documents both parameters fully. The description adds no additional meaning about parameters beyond what the schema provides (e.g., no context on commit message format or directory requirements). Baseline 3 is appropriate when schema does the heavy lifting.

    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's purpose with specific verbs ('review code changes using Gemini', 'commit if approved') and resources ('code changes', 'commit hash'). It distinguishes from the sibling 'review_only' by explicitly mentioning the conditional commit action and different return outcomes.

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

    Usage Guidelines5/5

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

    The description provides explicit usage guidance: use this tool when you want to review AND potentially commit code changes, with conditional logic (commit if approved/LGTM). It implicitly contrasts with 'review_only' by showing this tool includes commit functionality, making alternatives clear.

    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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  • 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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