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VPCriativo94

gemini-nano-banana-mcp

by VPCriativo94
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Server Quality Checklist

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

  • Latest release: v0.1.0

  • Disambiguation5/5

    Only one tool exists, so there is no possibility of confusion or overlap.

    Naming Consistency5/5

    The tool name 'generate_image' follows a clear verb_noun pattern and is descriptive.

    Tool Count3/5

    With only one tool, the server feels minimal, but it is focused on image generation so it is borderline acceptable.

    Completeness4/5

    The server covers the core image generation task, but lacks additional capabilities like editing or variations that might be expected in a full-featured image tool.

  • Average 4.2/5 across 1 of 1 tools scored.

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

    • No community issues in the last 6 months
    • 1 commit 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

  • Behavior4/5

    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 explicitly discloses the key side effect of saving the result as a local file and returning its path, which is essential for an agent to understand the tool's behavior. It does not mention potential errors or network dependencies, but the core behavioral trait is transparent.

    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, well-structured sentence that front-loads the primary action, includes the model context, and states the output. Every clause earns its place with no unnecessary fluff.

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

    Completeness4/5

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

    Given there is no output schema, the description covers the return value (the file path). It explains the core purpose and the side effect of file creation. The tool's complexity is low, with only one required parameter, and the description provides enough context for an agent to invoke it correctly. Minor gaps like error conditions are not critical.

    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?

    All four parameters are fully documented in the schema (100% coverage), so the description does not add significant parameter-level clarification beyond what the schema already provides. The mention of 'text prompt' and 'local file' loosely relates to prompt and file_name, but it does not add new meaning to individual parameters. Baseline of 3 is appropriate.

    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 uses a specific verb ('Generate') and resource ('an image'), clearly states the model family (Gemini image models) and the output behavior (saves to a local file, returns its path). This is unambiguous and distinguishes the tool from any potential image-related sibling, though none exist.

    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?

    There are no sibling tools to differentiate against, but the description clearly implies the usage scenario: converting a text prompt into an image. It does not explicitly state when not to use it or list alternatives, but given the absence of siblings and the self-explanatory nature, it provides clear context without exclusions.

    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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