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codeFi

mcp-nano-banana

by codeFi
OverviewSchemaRelated ServersScoreDiscussions
TypeScript
Hybrid

Server Quality Checklist

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

  • Latest release: v1.2.2

  • Disambiguation5/5

    The two tools are clearly distinct: generate_image produces a single image from text or references, while generate_favicons creates a package of favicon files. There is no overlap in their purposes, so an agent can easily choose between them.

    Naming Consistency5/5

    Both tool names follow the same verb_noun pattern (generate_image, generate_favicons). The naming is perfectly consistent and predictable.

    Tool Count4/5

    With only 2 tools, the set is on the smaller side, but it is appropriately scoped for a server dedicated to Nano Banana image generation. The two tools cover both general image generation and a specific use case (favicons), so the count feels intentional rather than insufficient.

    Completeness4/5

    The tool surface covers the core generation capabilities: text-to-image and image-to-image via references, plus a specialized favicon output. Minor gaps exist (e.g., no explicit editing/upscaling tool, no model listing), but the server's stated purpose is well-served.

  • Average 4/5 across 2 of 2 tools scored.

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

    • No community issues in the last 6 months
    • 7 commits in the last 12 weeks
    • Last stable release on
    • 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 are provided, so the description carries the full burden. It discloses that files and/or a ZIP bundle are written and mentions optional README, but does not cover return behavior (returnZipBase64), overwrite semantics, or behavior when output destinations are missing. This is partial disclosure.

    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?

    Two sentences, front-loaded with the main action and outputs. No filler, every clause adds useful signal. This is highly concise and well-structured.

    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?

    The tool has 11 parameters and no output schema, and while the description captures the core workflow, it leaves gaps about the interplay between outputDir, outputPath, and returnZipBase64. It also doesn't state what happens if no output destination is given, making it incomplete for complex usage.

    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 covers all 11 parameters with descriptions, so the baseline is 3. The description adds a high-level note about prompt vs imagePath and output formats, but doesn't enrich individual parameter semantics beyond what the schema already provides.

    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 opens with a specific verb+resource ('Generate a full favicon package') and enumerates concrete outputs (PNG sizes, web manifest, optional README, files/ZIP bundle). This clearly distinguishes it from the sibling generate_image, which is for generic image generation.

    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 defines the tool's purpose by contrasting with generate_image and stating the input modes (prompt or existing image). However, it does not explicitly name alternatives or state when not to use this tool, so it lacks a full 'when-to-use' guide.

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

  • Behavior3/5

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

    With no annotations, the description carries the full burden of behavioral disclosure. It discloses the AI model and the capability to accept up to 14 reference images, but doesn't address return behavior or potential pitfalls. The outputPath schema covers the return mechanism, so partial credit is warranted.

    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?

    Two sentences, front-loaded with the core action and resource. Every word contributes to understanding the tool's purpose and main capabilities.

    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?

    The description provides a high-level overview of the tool's capability, and the rich schema covers all parameters. Since there is no output schema, the description could mention the inline return behavior, but the outputPath parameter description already covers it. Adequate for a 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?

    Schema description coverage is 100%, so the schema already documents all parameters. The description only repeats the 'up to 14' reference image limit, which is already in the imagePaths parameter description, adding no new semantic value.

    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 tool's primary function: generating an image using the Google Nano Banana (Gemini) AI model. It distinguishes from the sibling tool generate_favicons by covering general image generation with text or reference images.

    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 explicitly identifies two usage modes: text-only generation and reference-image-guided generation. It does not mention alternatives or exclusions, but the context is clear enough for typical use cases.

    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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