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kodaimaehata

Cursor Reviewer MCP

by kodaimaehata
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Server Quality Checklist

58%
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  • Latest release: v1.0.0

  • Disambiguation5/5

    The two tools have clearly distinct purposes: codex.review uses Codex CLI, while cursor.review uses Cursor CLI with GPT-5. There is no overlap in functionality as each targets a different review system, making misselection unlikely.

    Naming Consistency5/5

    Both tools follow a consistent naming pattern: they use a prefix (codex or cursor) followed by a dot and the action 'review'. This pattern is uniform across all tools, making them predictable and easy to identify.

    Tool Count2/5

    With only 2 tools, the server feels thin for a 'Cursor Reviewer' domain, as it lacks operations like listing reviews, updating reviews, or handling review feedback. The count is too low to support comprehensive review workflows.

    Completeness2/5

    The tool surface is severely incomplete for a review system; it only provides review creation via two CLI methods, missing essential CRUD operations such as retrieving, updating, deleting reviews, or managing review-related data. This will likely cause agent failures in broader tasks.

  • Average 2.8/5 across 2 of 2 tools scored.

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

    • No community issues in the last 6 months
    • 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.

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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 full burden for behavioral disclosure. While it mentions the tool returns 'review JSON only', it doesn't describe what the review process entails, whether it's read-only or modifies files, what permissions are required, potential rate limits, or error conditions. The mention of 'timeout_ms' in the schema suggests this might be a long-running operation, but the description doesn't warn about this.

    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 extremely concise at just one sentence with no wasted words. It's front-loaded with the core purpose. However, the brevity comes at the cost of completeness - it could benefit from one more sentence about behavioral characteristics given the lack of annotations.

    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 6 parameters (3 required), no annotations, and no output schema, the description is inadequate. It doesn't explain what the review process does, what the output looks like, how it differs from the sibling tool, or any behavioral characteristics. The schema handles parameter documentation, but the description fails to provide the necessary context for proper tool understanding and usage.

    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 all parameters are documented in the schema. The description adds no additional parameter semantics beyond what's already in the schema descriptions. The baseline score of 3 reflects adequate coverage through the schema alone, though the description doesn't enhance understanding of how parameters interact or their practical usage.

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

    Purpose3/5

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

    The description states the tool 'Review deliverables via Codex CLI and return review JSON only', which provides a verb ('Review') and resource ('deliverables'), but is vague about what 'deliverables' specifically means and how it differs from the sibling tool 'cursor.review'. It doesn't clearly distinguish between the two review tools, leaving ambiguity about when to use one over the other.

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

    Usage Guidelines2/5

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

    The description provides no guidance on when to use this tool versus the sibling 'cursor.review' tool, nor does it mention any prerequisites, exclusions, or alternative approaches. Usage context is implied at best through the mention of 'Codex CLI', but this is insufficient for effective tool selection.

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

  • 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. It mentions the tool runs 'via Cursor CLI' and returns 'review JSON only', but lacks critical details such as whether this is a read-only or mutating operation, authentication requirements, rate limits, error handling, or what the review process entails (e.g., automated analysis, human-in-the-loop).

    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 a single sentence that efficiently conveys the core action, mechanism, and output. It is front-loaded with essential information and contains no redundant or unnecessary details.

    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?

    Given the complexity of a review tool with 6 parameters, no annotations, and no output schema, the description is incomplete. It fails to explain the review process, expected outcomes, error conditions, or how the output JSON is structured, leaving significant gaps for an AI agent to understand and use 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 parameters thoroughly. The description adds no additional semantic context about parameters beyond what's in the schema, such as explaining how 'targets' and 'reference' interact or the purpose of 'previous_reviews'. Baseline 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.

    Purpose4/5

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

    The description clearly states the action ('Review deliverables') and the mechanism ('via Cursor CLI (GPT‑5)'), and specifies the output format ('return review JSON only'). It distinguishes from the sibling 'codex.review' by specifying the Cursor platform and GPT-5 model, though it doesn't explicitly contrast their differences.

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

    Usage Guidelines2/5

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

    The description provides no guidance on when to use this tool versus the sibling 'codex.review' or other alternatives. It mentions the platform and model but doesn't explain the specific use cases, prerequisites, or scenarios where this tool is preferred over others.

    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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