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henry2craftman

Gemini Collaboration MCP Server

by henry2craftman
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TypeScript
Remote

Server Quality Checklist

67%
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: collaborate_on_code is for iterative code development through a structured collaboration process, while consult_gemini is for seeking validation, advice, or a second opinion on decisions or technical problems. There is no overlap in functionality or ambiguity between them.

    Naming Consistency5/5

    Both tools follow a consistent verb_noun pattern: collaborate_on_code and consult_gemini. The naming is predictable and readable, with no deviations or mixed conventions, making it easy for agents to understand the action and target.

    Tool Count2/5

    With only 2 tools, the server feels thin for its purpose of 'Gemini Collaboration,' which suggests a broader scope involving code development and consultation. While the tools are well-defined, the count is too low to provide comprehensive coverage for collaboration workflows, lacking tools for specific actions like reviewing code, managing iterations, or handling feedback loops.

    Completeness2/5

    The server has significant gaps in its tool surface for collaboration. It lacks tools for key operations such as reviewing or editing code independently, managing project states, or handling iterative feedback beyond the initial collaboration. This incomplete coverage will likely cause agent failures when trying to perform nuanced or multi-step collaboration tasks.

  • Average 3.3/5 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. It outlines the collaborative steps (PRD creation, tech stack decision, code generation/refinement) but fails to disclose critical traits like whether this is a read-only or mutating operation, authentication requirements, rate limits, or what happens to intermediate outputs. The description is insufficient for a tool with complex multi-step behavior.

    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 with three sentences that efficiently outline the collaboration process and outcome. It's front-loaded with the core purpose, though the numbered steps could be slightly more concise. Overall, it avoids unnecessary elaboration.

    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's complex multi-step collaboration process, no annotations, and no output schema, the description is incomplete. It doesn't explain the format or structure of the returned 'final code', what happens during the iterative dialogue, or how errors or interruptions are handled. The description fails to compensate for the lack of structured metadata.

    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% with one parameter 'request' documented as 'What to build'. The description adds no additional parameter semantics beyond what the schema provides, maintaining the baseline score of 3 since the schema adequately covers the single parameter.

    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 enables collaboration with Gemini to develop code through iterative dialogue, specifying the verb 'collaborate' and resource 'code'. It distinguishes from the sibling 'consult_gemini' by emphasizing joint development rather than consultation, though it doesn't explicitly contrast them.

    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 implies usage for code development projects requiring iterative collaboration, but provides no explicit guidance on when to use this tool versus 'consult_gemini' or other alternatives. It mentions the collaborative process but lacks clear exclusions or prerequisites.

    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. While it mentions the tool's purpose and usage scenarios, it doesn't disclose important behavioral traits like whether this is a read-only operation, potential costs/rate limits, response format expectations, or any authentication requirements. The description is insufficient for a tool with zero annotation coverage.

    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 well-structured with a clear purpose statement followed by a bulleted list of usage scenarios. It's appropriately sized and front-loaded with the core purpose. The bullet points could potentially be more concise, but overall the structure is effective with minimal wasted space.

    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 consulting an AI system, the lack of annotations, and no output schema, the description is incomplete. It doesn't address important contextual factors like response format, error conditions, rate limits, or what constitutes a successful consultation. For a tool that interacts with an external AI service, more behavioral context is needed.

    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 has 100% description coverage, with both parameters ('query' and 'context') clearly documented in the schema itself. The tool description doesn't add any parameter-specific information beyond what's already in the schema, so it meets the baseline of 3 for high schema coverage without adding extra value.

    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: 'Consult Gemini AI for a second opinion or advice.' This specifies the action (consult) and resource (Gemini AI). However, it doesn't explicitly differentiate from the sibling tool 'collaborate_on_code' - both could involve AI assistance, so the distinction isn't articulated.

    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 usage guidelines with a bulleted list of four specific scenarios: validation of approach, uncertainty about decisions, different perspectives, and expert technical advice. This gives clear guidance on when to use this tool, though it doesn't mention when NOT to use it or explicitly contrast with the sibling tool.

    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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