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dakrin

Gemini MCP Server

by dakrin
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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 completely distinct purposes: generateWithGemini is for content generation, while getModelInfo is for retrieving model metadata. There is no overlap in functionality, making it impossible to confuse them.

    Naming Consistency4/5

    Both tools use camelCase naming, which is consistent. However, generateWithGemini uses a verb+preposition+noun pattern, while getModelInfo uses verb+noun, creating a minor deviation in structure.

    Tool Count3/5

    With only 2 tools, the server feels thin for a Gemini API server, as it lacks operations like listing models, managing conversations, or handling multimodal inputs. The count is borderline for the apparent scope of interacting with a generative AI model.

    Completeness2/5

    The tool surface is severely incomplete for a Gemini API server. It only covers content generation and model info, missing essential operations such as chat/completion management, file uploads for multimodal inputs, or configuration settings, which will likely cause agent failures in complex workflows.

  • Average 2.9/5 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 Apache 2.0.

  • 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 mentions 'beta API' which hints at potential instability, but doesn't describe rate limits, authentication requirements, cost implications, response format, or error behavior. For a generative AI tool with no annotation coverage, this leaves significant gaps in understanding how it behaves.

    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 a single, efficient sentence that gets straight to the point. It's appropriately sized for a tool with good schema documentation, though it could potentially be more front-loaded with key usage information.

    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 generative AI tool with no annotations and no output schema, the description is insufficient. It doesn't explain what the tool returns, error conditions, rate limits, or important behavioral characteristics. The 'beta API' mention provides some context but doesn't compensate for the lack of structured information about this 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 the schema already documents all 5 parameters thoroughly. The description adds no additional parameter information beyond what's in the schema. According to guidelines, when schema coverage is high (>80%), the baseline is 3 even with no param info in the description.

    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 action ('Generate content') and specifies the model ('Gemini 2.5 Pro Experimental'), but it's somewhat vague about what type of content is generated (text, code, etc.) and doesn't clearly differentiate from the sibling tool 'getModelInfo'. The description provides basic purpose but lacks specificity about the resource or output.

    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 'getModelInfo' or any alternatives. It mentions it's a 'beta API' which implies potential instability, but doesn't give explicit when/when-not instructions or prerequisites for 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 burden for behavioral disclosure. While 'Get information' implies a read-only operation, it doesn't specify what kind of information is returned, whether there are rate limits, authentication requirements, or any other behavioral characteristics. The description is minimal and lacks operational context.

    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, efficient sentence that gets straight to the point with no wasted words. It's appropriately sized for a simple tool with no parameters and effectively communicates the core purpose.

    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?

    For a zero-parameter tool with no output schema, the description is minimally adequate but lacks important context. It doesn't explain what information about the model is returned, the format of the response, or how this information might be useful to an agent. The absence of annotations means the description should provide more operational guidance.

    Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

    Parameters4/5

    Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

    The tool has zero parameters with 100% schema description coverage, so the baseline is 4. The description appropriately doesn't discuss parameters since none exist, which is correct for this tool configuration.

    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 ('Get information') and the target resource ('Gemini model being used'), making the purpose immediately understandable. However, it doesn't distinguish this from its sibling tool 'generateWithGemini' which likely generates content rather than retrieving model metadata.

    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. There's no mention of when this information would be needed, what context it applies to, or how it differs from the sibling 'generateWithGemini' 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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