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crazyrabbitLTC

Code Review MCP Server

by crazyrabbitLTC
OverviewSchemaRelated ServersScoreDiscussions
JavaScript
Remote

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 have clearly distinct purposes: analyze_repo provides a high-level structural overview, while code_review offers detailed analysis with specific feedback. There is no overlap in functionality, and the descriptions explicitly differentiate when to use each tool.

    Naming Consistency5/5

    Both tools follow a consistent verb_noun naming pattern (analyze_repo and code_review), using snake_case throughout. The naming is predictable and aligns well with their described functionalities.

    Tool Count2/5

    With only 2 tools, the server feels thin for a 'Code Review MCP Server' domain. While the tools cover analysis and review, the scope suggests potential gaps in operations like managing reviews, tracking issues, or integrating with version control, making the set appear incomplete for the stated purpose.

    Completeness2/5

    The tool set is severely incomplete for code review workflows. It lacks essential operations such as creating, updating, or deleting reviews; commenting on code; or handling pull requests. Agents will face dead ends when trying to perform common code review tasks beyond basic analysis.

  • Average 4.2/5 across 2 of 2 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 status not available

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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 describes the tool's behavior ('performs in-depth analysis', 'returns structured results including issues found, their severity, recommendations') but lacks details on permissions needed, rate limits, error handling, or whether it modifies the codebase. It adequately covers the core operation but misses some behavioral traits.

    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 appropriately sized and front-loaded, with the first sentence clearly stating the purpose and key features. It uses two sentences efficiently, though the second sentence could be slightly more concise by combining some clauses without losing clarity.

    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 complexity of a code review tool with 5 parameters, no annotations, and no output schema, the description is fairly complete. It covers purpose, usage, and output structure, but could benefit from more details on behavioral aspects like execution time or limitations to fully compensate for the lack of annotations and output schema.

    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 schema description coverage is 100%, so the schema already documents all parameters. The description adds context by mentioning 'specific files' and 'focus areas' like security and performance, which align with parameters, but doesn't provide additional semantics beyond what the schema offers. Baseline 3 is appropriate as the 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 ('perform in-depth analysis', 'returns structured results') and resources ('repository or specific files'), distinguishing it from the sibling tool 'analyze_repo' by emphasizing comprehensive review with specific feedback areas like security, performance, and maintainability.

    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 explicitly states when to use the tool ('when you need a comprehensive code review', 'when you need actionable insights to improve code quality or when evaluating a codebase for potential problems'), providing clear context and distinguishing it from alternatives without being misleading.

    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 provided, the description carries the full burden of behavioral disclosure. It explains the tool's behavior ('flattens the repository into a textual representation') and output format ('high-level overview'), which is helpful. However, it doesn't mention potential limitations like file size constraints, processing time, error conditions, or authentication requirements that would be important for a tool analyzing code repositories.

    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 efficiently structured with three sentences that each serve a distinct purpose: stating the tool's purpose, explaining its behavior, and providing usage guidelines. There's no redundant information, and the most important guidance (when to use the tool) is front-loaded. Every sentence earns its place by adding value.

    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 moderate complexity (3 parameters, no output schema, no annotations), the description provides good contextual coverage. It explains the tool's purpose, behavior, and relationship to the sibling tool. However, without annotations or output schema, it could benefit from more detail about what the 'textual representation' output actually contains and any limitations or requirements for using the tool effectively.

    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 all three parameters thoroughly. The description doesn't add any parameter-specific information beyond what's in the schema. It mentions analyzing 'specific files' and 'file types' generally but provides no additional syntax, format, or usage guidance for these parameters. The baseline score of 3 is appropriate when the 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: 'analyze a code repository structure without performing a detailed review' and 'flattens the repository into a textual representation'. It specifies the verb ('analyze'), resource ('code repository'), and scope ('high-level overview of code organization, directory structure, and file contents'), distinguishing it from the sibling tool 'code_review' which implies more detailed analysis.

    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 guidance on when to use this tool: 'Use this tool when you need to analyze a code repository structure without performing a detailed review' and 'Use it before code_review when you need to understand the codebase structure first, or when a full code review is not needed'. It clearly differentiates from the alternative sibling tool 'code_review' and specifies both appropriate and inappropriate contexts.

    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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