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kevinten-ai

Gemini Image Gen MCP Server

by kevinten-ai
OverviewSchemaRelated ServersScoreDiscussions
Python
Remote

Server Quality Checklist

67%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v1.3.0

  • Disambiguation5/5

    Only one tool exists, so there is zero ambiguity in tool selection. The agent can only choose this single tool.

    Naming Consistency5/5

    With a single tool, naming consistency is trivially maintained. The verb_noun pattern 'generate_image' is clear and follows convention.

    Tool Count3/5

    One tool is on the low end of what's reasonable for a server. While the server has a narrow purpose (image generation), it may be too restrictive for agents needing additional capabilities like model listing or image variation.

    Completeness5/5

    The tool fully covers the server's stated purpose of generating an image from a text prompt. No obvious missing functionality within the declared scope.

  • Average 4/5 across 1 of 1 tools scored.

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

    • No community issues in the last 6 months
    • 3 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.

  • 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.

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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

  • Behavior3/5

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

    No annotations provided, so description carries full burden. It discloses the provider, default model, and available models, and a tip on handling quota errors. However, it lacks details on whether the tool has side effects (e.g., state changes), authentication requirements, rate limits, or output format (e.g., URL vs base64). The tip about quota is useful but not comprehensive.

    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?

    Three sentences: first states purpose, second provides provider and default, third lists models and tip. Efficient and front-loaded with no redundant 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?

    Despite good input schema coverage, the tool lacks an output schema and the description does not explain what the tool returns (e.g., image URL, binary, or file). Also missing info about safety filters, content policies, aspect ratio, or any other generation parameters beyond prompt and model. This leaves agents guessing the response format.

    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?

    Schema coverage is 100% with both parameters described. The description adds value by listing available models explicitly and providing a tip about retrying on quota errors, which helps agents choose model parameter wisely. This goes beyond the schema's enum list.

    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 verb "generate" and the resource "image from a text prompt" with specific provider (ai-studio) and default model. It distinguishes itself well even without sibling tools.

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

    Usage Guidelines4/5

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

    Provides clear context that it uses Google Gemini or Imagen, and offers a practical tip for quota errors by retrying with different models. However, it does not explicitly state when not to use this tool or provide alternative scenarios.

    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 the MCP server is working as expected.
  • Confirm that there are no obvious security issues.
  • Evaluate tool definition quality.

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