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praneybehl

Claude Code Review MCP

by praneybehl

Server Quality Checklist

67%
Profile completionA complete profile improves this server's visibility in search results.
  • Latest release: v1.0.0

  • Disambiguation5/5

    With only one tool, there is no possibility of ambiguity or overlap between tools. The tool's purpose is clearly defined and distinct by default.

    Naming Consistency5/5

    A single tool inherently has perfect naming consistency, as there are no other tools to compare it against. The name 'perform_code_review' follows a clear verb_noun pattern.

    Tool Count2/5

    A single tool for a code review server feels thin and incomplete for the domain. While it covers the core action, typical code review workflows might include additional operations like listing reviews, commenting, or approving changes.

    Completeness2/5

    The server is severely incomplete for code review functionality. It only performs reviews but lacks tools for managing reviews (e.g., get, update, delete), interacting with review comments, or handling review states, which are essential for a full code review workflow.

  • Average 3.6/5 across 1 of 1 tools scored.

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

    • 0 of 1 community issues answered or closed 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 is failing
  • 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.

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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

  • Behavior2/5

    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. While it mentions the git repository requirement, it doesn't describe what the tool actually does behaviorally - whether it makes API calls to LLMs, what format the review output takes, whether it modifies files, or any rate limits/authentication needs. For a complex 8-parameter tool with no annotations, this is a significant 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 extremely concise with just two sentences that both earn their place. The first sentence states the core purpose, and the second provides essential operational context. There's zero waste or redundancy.

    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?

    For a complex tool with 8 parameters performing code review via LLMs, the description is insufficiently complete. With no annotations, no output schema, and no behavioral details, it leaves critical gaps about what the tool actually produces, how it behaves, and what the user should expect. The description should explain the review output format and operational behavior.

    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 description provides no parameter-specific information beyond the git context hint. However, with 100% schema description coverage where all 8 parameters have clear descriptions in the schema itself, the baseline score of 3 is appropriate. The description doesn't add value beyond what's already documented in the structured schema.

    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 ('performs a code review') and resources ('using a specified LLM on git changes'). It distinguishes the tool's scope by specifying it works on git changes and requires being run from a git repository root, making it highly specific even without sibling tools for comparison.

    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 provides clear context about when to use the tool ('requires being run from the root of a git repository'), which is essential operational guidance. However, it doesn't mention when NOT to use it or suggest alternatives, which would be needed for a perfect score since there are no sibling tools to differentiate from.

    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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  • Evaluate tool definition quality.

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