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priorwave

nano-banana

by priorwave
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Server Quality Checklist

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

  • Latest release: v1.0.0

  • Disambiguation5/5

    Only one tool exists, so there is no possibility of confusing it with other tools. Its purpose is clearly defined.

    Naming Consistency5/5

    With a single tool, the naming is inherently consistent and follows a clear verb_noun pattern (generate_image).

    Tool Count3/5

    A single tool feels thin for a server, but it may be appropriate if the server's entire purpose is image generation. Still, it is borderline and likely lacks broader functionality.

    Completeness4/5

    The tool provides the core image generation capability with no obvious dead ends, though a more complete surface might include options like output path or resolution controls. Minor gaps exist but are workable.

  • 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
    • 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

  • Behavior4/5

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

    Annotations already indicate the tool is not read-only and has side effects. The description adds context by naming the external service (Google Gemini) and the side effect of saving a JPEG to disk. It also clarifies the output format, going beyond what annotations provide.

    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 sentence that is directly relevant and front-loaded with the main action. It avoids unnecessary detail and clearly communicates the purpose and key behavior.

    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 the tool's simplicity (2 parameters, output schema present, annotations provided), the description is sufficiently complete. It mentions the key side effect (saving to disk) and the technology (Gemini). A minor gap is the lack of any error handling or rate-limit information, but this is not critical for a straightforward generation 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?

    The input schema provides full descriptions for both 'prompt' and 'save_path', covering 100% of parameters. The description itself does not add extra semantics about parameters beyond what is in the schema, so it aligns with the baseline for high coverage.

    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 action ('Generate an image from a text prompt using Google Gemini') and the output ('save the JPEG to disk'). It is specific and leaves no ambiguity about what the tool does. Even without siblings, it distinguishes itself by naming the model and output format.

    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?

    The description implicitly tells when to use this tool: whenever an image needs to be generated from a text prompt and saved to disk. It does not explicitly mention exclusions or alternatives because there are no sibling tools, so this is acceptable. It provides clear functional context.

    Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

GitHub Badge

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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