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jiwon86

Nano Banana MCP

by jiwon86
OverviewSchemaRelated ServersScoreDiscussions
Python
Remote

Server Quality Checklist

58%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v0.1.0

  • Disambiguation5/5

    The two tools are clearly distinct: one provides metadata about available models and usage notes, while the other performs the actual image generation/editing. There is no overlap or ambiguity between them.

    Naming Consistency4/5

    Both tools share the consistent 'nano_banana_' prefix and use snake_case. 'generate_image' follows verb_noun pattern, while 'models' is a simple plural noun rather than a verb-led name, causing a slight inconsistency.

    Tool Count3/5

    With only two tools, the surface feels thin, but it is understandable for a narrowly scoped server dedicated to one image generation model. It borders on minimal yet is not unreasonable.

    Completeness4/5

    The core functionality of generating/editing images is covered, along with model discovery. Missing features like generation history or job status are not obvious gaps for a simple synchronous API, so it is mostly complete.

  • 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
    • 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

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  • 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

  • Behavior3/5

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

    With no annotations provided, the description carries the full burden of behavioral disclosure. It explains the modal behavior (generation vs editing) but does not mention side effects (e.g., writing to output_dir), rate limits, auth requirements, or what happens with optional flags like web search. This is useful context but lacks depth.

    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 short paragraphs that front-load the primary purpose and immediately provide usage modes. Every sentence adds value, with no filler or repetition. This is an exemplar of concise tool documentation.

    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?

    Given the tool's complexity (10 parameters, multiple execution modes, and an output schema), the description is incomplete. It adequately explains the two invocation modes but does not guide the agent on how to choose models, when to set aspect_ratio or image_size, or what the optional boolean flags do. The presence of an output schema reduces the need to explain return values, but the tool's parameter richness demands more guidance than this description provides.

    Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

    Parameters2/5

    Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

    With schema_description_coverage at 0%, the description must compensate for the 10 parameters. It only explains the role of 'prompt' and 'image_paths', leaving the other eight parameters (model, image_size, aspect_ratio, include_text, enable_web_search, request_timeout_s, enable_image_search, output_dir) without any descriptive benefit beyond their schema titles. The two explained parameters are helpful, but the mismatch is significant.

    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 'Generate or edit images with Nano Banana', which clearly states the tool's action (generate/edit) and resource (images). It further distinguishes two modes (text-to-image vs image editing) and is unambiguous when compared to the sibling 'nano_banana_models'.

    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 instructs when to pass only 'prompt' versus 'image_paths' plus 'prompt', giving clear context for each invocation mode. However, it does not mention when to prefer an alternative tool or explicitly exclude certain scenarios, so it stops short of a perfect 5.

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

  • Behavior4/5

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

    With no annotations, the description must convey safety. 'Return' clearly indicates a read-only operation, and mentioning 'usage notes' hints at practical constraints. It does not explicitly state that no side effects occur, but the semantic is clear enough for a listing tool.

    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?

    A single, concise sentence that front-loads the verb and resource. No wasted words or redundant information.

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

    Completeness5/5

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

    The tool is simple with no parameters and an output schema, so the description's mention of both ids and usage notes is fully adequate. The sibling tool context further clarifies its role.

    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?

    The tool has zero parameters and schema coverage is 100%. The description adds no parameter details, but none are needed. A baseline of 4 is appropriate for a parameter-free tool.

    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 a specific action ('Return') and a precise result ('supported Nano Banana model ids and usage notes'). It unambiguously identifies this as a listing/metadata tool, distinct from the sibling generation tool.

    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?

    No explicit guidance on when to use this tool versus nano_banana_generate_image is provided. The intended usage is implied (check available models before generation) but not directly stated, so the description earns a mid-level score.

    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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