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miharu8686

mutual-review-mcp

by miharu8686
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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: v0.1.1

  • Disambiguation5/5

    Each tool targets a distinct input type (code snippet, diff string, file), with clear descriptions that prevent any ambiguity.

    Naming Consistency5/5

    All tools follow a consistent verb_noun pattern using 'review_' prefix followed by the input type, making them predictable and easy to understand.

    Tool Count5/5

    Three tools is perfectly scoped for a focused code review server, covering the essential input types without unnecessary bloat.

    Completeness5/5

    The tool set covers all common code review scenarios (snippet, diff, file), providing complete lifecycle coverage for the stated purpose.

  • Average 3.6/5 across 3 of 3 tools scored.

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

    • No community issues in the last 6 months
    • 1 commit in the last 12 weeks
    • Last stable release on
    • 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.

  • Add a glama.json file to provide metadata about your server.

  • If you are the author, simply .

    If the server belongs to an organization, first add glama.json to the root of your repository:

    {
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}

    Then . Browse examples.

  • Add related servers to improve discoverability.

How to sync the server with GitHub?

Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.

To manually sync the server, click the "Sync Server" button in the MCP server admin interface.

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?

    No annotations are provided, so the description carries the full burden. It discloses that no git invocation is performed, but does not mention any side effects, authentication requirements, rate limits, or what the output (review comments) looks like.

    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?

    Two sentences convey the core purpose and key usage note without any unnecessary words. The structure is front-loaded with the primary action.

    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 tool has three parameters and no output schema or annotations, the description omits important details such as expected output format, error conditions, prerequisites, and behavior when optional parameters are omitted. It is not sufficient for an agent to confidently invoke the tool.

    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 coverage is 100% with basic descriptions for all parameters. The description adds only minor clarification that 'diff' should be a unified diff string and that 'synthesize' generates a report. It does not significantly enhance understanding beyond the schema.

    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 it performs a mutual code review on a unified diff string. It differentiates from siblings like 'review_code' and 'review_file' by specifying that the diff text should be passed directly, not a file or code snippet.

    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 indicates when to use the tool (when a unified diff string is available) and clarifies that no git invocation is performed. However, it does not explicitly state when not to use it or provide alternatives to the siblings.

    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?

    Description reveals the multi-model review process (Claude and GPT-4o) and synthesis, adding transparency beyond no annotations. However, it does not state whether the tool modifies the file or is read-only, nor does it mention any side effects.

    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?

    Two sentences, front-loaded with purpose, efficient and clear with no wasted words.

    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?

    Description explains the process but lacks detail on the output format or return value. Since no output schema exists, the agent needs more information about what the synthesis report looks like. Parameter coverage is good but completeness is moderate for a tool with four parameters.

    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 coverage is 100%, providing baseline 3. Description does not explain parameters beyond what schema already provides (e.g., what 'context' or 'synthesize' means in practice). No added value.

    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?

    Description clearly states 'Mutual code review on a file', specifying the action (review) and resource (file). It distinguishes from sibling tools like review_code and review_diff by focusing on file-level review.

    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?

    Description implies use for file review but does not explicitly state when to choose this tool over review_code or review_diff. No direct guidance on prerequisites or exclusions.

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

  • Behavior4/5

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

    No annotations provided, so description carries full burden. It discloses the multi-model review process and synthesis step, providing useful behavioral context. However, it omits details like return format, side effects, or authorization needs.

    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 a single sentence that is front-loaded and contains zero wasted words. Every part conveys essential information about the tool's operation.

    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?

    With 5 parameters, no output schema, and sibling tools, the description explains the process but lacks usage context and return value information. It is adequate for a simple tool but has gaps in guiding selection among siblings.

    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 baseline is 3. The tool description does not add extra meaning beyond the schema descriptions for parameters like 'code', 'context', 'filename', 'language', and 'synthesize'.

    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 performs a mutual code review on a code snippet, specifying two models (Claude and GPT-4o) review independently then synthesize. It distinguishes from siblings 'review_diff' and 'review_file' by focusing on a code snippet rather than a diff or file.

    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?

    No explicit guidance on when to use this tool versus alternatives. The description implies it is for standalone code snippets, but does not mention when not to use or provide criteria to differentiate from 'review_diff' or 'review_file'.

    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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