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nicobailon

Gemini Code Review MCP

by nicobailon
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Python
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Server Quality Checklist

58%
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  • Latest release: v0.5.0

  • Disambiguation3/5

    While ask_gemini is a general-purpose query tool, generate_ai_code_review and generate_pr_review are more specialized for code review, leading to potential overlap where an agent might choose the wrong tool for a review task. Descriptions help differentiate, but ambiguity remains.

    Naming Consistency4/5

    All tool names use snake_case and follow a verb_noun pattern consistently. The only minor deviation is the use of 'ask' versus 'generate' as verbs, but overall the naming is predictable and clear.

    Tool Count4/5

    With three tools, the server covers the core functionality of generating code reviews and querying Gemini, which is appropriate for a focused code review assistant. The count is slightly low but not unreasonable for its scope.

    Completeness4/5

    The tool set covers the main use cases: general query, AI code review from context, and PR-specific review. Minor gaps like review history or configuration management are absent but not critical for the server's stated purpose.

  • Average 2.7/5 across 3 of 3 tools scored.

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

    • No community issues in the last 6 months
    • 0 commits 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.

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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

  • Behavior1/5

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

    With no annotations, the description must disclose behavioral traits. It only states 'Generate code review... with configuration discovery' without explaining side effects, authentication needs, rate limits, or output format. This is a critical gap.

    Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

    Conciseness3/5

    Is the description appropriately sized, front-loaded, and free of redundancy?

    The description is a single sentence, which is concise, but it lacks structure and front-loads no key information beyond the tool name. It is not optimally organized for an AI agent.

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

    Completeness1/5

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

    Given the tool's complexity (13 parameters, multiple flags), the description is severely incomplete. It omits prerequisites (e.g., GitHub access), output details, and ignores the output schema. The agent cannot judge completeness.

    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 baseline is 3. The description adds minimal value beyond the schema, only mentioning 'configuration discovery' vaguely. It does not enhance parameter understanding.

    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: generating a code review for a GitHub Pull Request. However, it does not differentiate from the sibling tool 'generate_ai_code_review', preventing a top score.

    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?

    No guidance is provided on when to use this tool versus the siblings 'ask_gemini' or 'generate_ai_code_review'. The description lacks any context for appropriate usage.

    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 full responsibility for behavioral transparency. It only states the tool generates a code review without disclosing any side effects, state changes, or security implications. For a tool with 18 parameters and multiple execution modes, this is insufficient.

    Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

    Conciseness3/5

    Is the description appropriately sized, front-loaded, and free of redundancy?

    The description is a single sentence, which is concise but lacks important context for a complex tool. It is front-loaded with the verb and resource, but the brevity sacrifices completeness. Every word earns its place, but the sentence could be expanded to include key usage notes.

    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?

    Despite high schema coverage and an output schema, the description fails to explain the output nature (e.g., a markdown report) or guide the agent on selecting the appropriate input mode. For a tool with 18 parameters and multiple sources, the description is too sparse to be fully complete.

    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% parameter description coverage, so the schema already documents each parameter. The description adds no extra meaning beyond what the schema provides, such as parameter interactions (e.g., precedence when multiple sources are given) or defaults interplay.

    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 generates AI-powered code reviews from three sources: context file, content, or project analysis. However, it does not explicitly differentiate from sibling tools like 'ask_gemini' and 'generate_pr_review', missing an opportunity to clarify when to use this tool over others.

    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 alternatives, nor does it specify when to choose one input source over another (context_file_path vs context_content vs project_path). No exclusions or prerequisites are mentioned.

    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?

    No annotations provided, so description carries full burden. It mentions combining context generation with an API call but fails to disclose behavioral traits like file reading, potential mutations (none), or authentication needs. Insufficient transparency for a tool with 10 parameters.

    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?

    Two sentences, both informative. First sentence states primary function, second adds context. No fluff, front-loaded. Could include a brief usage hint, but overall efficient.

    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?

    Despite 10 parameters and output schema, description is minimal. Missing explanation of tool's workflow (context generation then API call), order of operations, or how parameters interact. Incomplete for a complex 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 description coverage is 100%, so baseline is 3. Description adds minimal extra meaning beyond schema—just 'context from files' which aligns with file_selections. No additional parameter semantics or examples provided.

    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 generates context from files and sends to Gemini, specifying a verb ('generates context') and resource ('Gemini'). It differentiates from sibling tools (code review, PR review) by focusing on general AI query with file context.

    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?

    No guidance on when to use this tool versus alternatives. The description lacks explicit context for appropriate usage, such as when to choose this over direct Gemini calls or other context-generation tools.

    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 there are no obvious security issues.
  • Evaluate tool definition quality.

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