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saroby

Nano Banana

by saroby
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TypeScript
Remote

Server Quality Checklist

67%
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: generate_image creates new images, while list_images retrieves existing ones. There is no overlap in functionality, making it impossible for an agent to confuse them.

    Naming Consistency5/5

    Both tools follow a consistent verb_noun pattern (generate_image, list_images) with clear, descriptive names. The naming convention is uniform and predictable throughout the set.

    Tool Count2/5

    With only two tools, the server feels severely under-scoped for an image generation domain. There are obvious gaps like deleting, updating, or managing images beyond listing, making it difficult for agents to perform complete workflows.

    Completeness2/5

    The tool surface is highly incomplete for image generation and management. While it covers creation and listing, it lacks essential operations such as deleting images, updating metadata, or viewing detailed image properties, which will cause agent failures in many scenarios.

  • Average 3.2/5 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 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

  • Behavior2/5

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

    No annotations are provided, so the description carries full burden. It mentions the directory parameter but doesn't disclose behavioral traits like what 'List' returns (e.g., file paths, metadata, pagination), error handling, or performance characteristics. For a tool with no annotation coverage, this leaves key operational details unspecified.

    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 directly states the tool's purpose without unnecessary words. It's appropriately sized for a simple tool and front-loaded with the core action, making it easy to parse quickly.

    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?

    Given the tool's low complexity (1 parameter, no output schema, no annotations), the description is minimally adequate but incomplete. It covers the basic purpose but lacks details on usage guidelines, behavioral transparency, and output expectations, which are needed for effective agent operation despite the simple context.

    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%, with the parameter 'directory' fully documented in the schema. The description adds no additional meaning beyond implying the directory contains 'generated images', which is already suggested by the tool name. Baseline 3 is appropriate as the schema does the heavy lifting, but the description doesn't compensate or enhance understanding.

    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 ('List') and resource ('generated images'), specifying the scope as 'in the specified directory'. It distinguishes from the sibling tool 'generate_image' by focusing on retrieval rather than creation. However, it doesn't explicitly contrast with the sibling beyond the different verb, missing a direct comparison statement.

    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 minimal guidance, stating only the basic context ('in the specified directory'). It doesn't explain when to use this tool versus alternatives, mention prerequisites, or provide any exclusions. With a sibling tool available, this lack of comparative guidance is a significant gap.

    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?

    No annotations are provided, so the description carries the full burden of behavioral disclosure. While it mentions model characteristics (fast vs. high quality), it doesn't cover critical behavioral aspects like authentication requirements, rate limits, cost implications, file output behavior, or error handling. For a generative AI tool with no annotation coverage, this is insufficient.

    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 extremely concise (two sentences) and front-loaded with the core purpose. Every sentence adds value: the first establishes the tool's function, and the second provides model differentiation. There's zero wasted text or redundancy.

    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?

    Given the complexity of an image generation tool with 6 parameters and no annotations or output schema, the description is incomplete. It covers the basic purpose and model options but lacks information about output format, file handling, error cases, or integration context. The schema handles parameter documentation, but the description should provide more operational context.

    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 fully documents all 6 parameters. The description adds minimal value by briefly explaining the model options ('flash' for fast, 'pro' for high quality), but doesn't provide additional semantic context beyond what's in the schema. This meets the baseline of 3 when schema coverage is high.

    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 tool's purpose: 'Generate images using Google Gemini models.' It specifies the action (generate) and resource (images) with the technology context (Google Gemini models). However, it doesn't explicitly differentiate from the sibling 'list_images' tool, which would require a 5.

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

    Usage Guidelines3/5

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

    The description provides some usage context by explaining the two model options ('flash' for fast, 'pro' for high quality), which implies when to choose each. However, it doesn't explicitly state when to use this tool versus the sibling 'list_images' or provide any exclusion criteria or 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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  • Evaluate tool definition quality.

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