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NH-5

Nano Banana MCP Server

by NH-5
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TypeScript
Hybrid

Server Quality Checklist

50%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v1.0.0

  • Disambiguation5/5

    With only one tool, there is no possibility of ambiguity or overlap between tools. The single tool 'generate_image' has a clearly distinct purpose that cannot be confused with any other tool in this set.

    Naming Consistency5/5

    The single tool name 'generate_image' follows a clear verb_noun pattern. With only one tool, there is perfect consistency as there are no other tools to compare against or create naming conflicts with.

    Tool Count2/5

    A single tool for an image generation server feels thin and under-scoped. While it might cover the core functionality, typical MCP servers for such domains would include additional tools like list_models, get_image_details, or variations of generation parameters. The count of 1 suggests limited capability for agents to perform related operations.

    Completeness2/5

    The tool surface is severely incomplete for an image generation domain. There is only a generation tool with no supporting operations like model listing, configuration management, image retrieval, or editing capabilities. This creates significant gaps that will likely cause agent failures when trying to perform comprehensive image-related tasks.

  • Average 2.9/5 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

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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?

    With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions the model but fails to describe key traits like rate limits, authentication needs, output format (e.g., image type), or whether the operation is idempotent. This leaves significant gaps for a generative AI 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?

    The description is a single, efficient sentence with zero wasted words. It's front-loaded with the core function and includes necessary model specification, making it appropriately concise for this tool.

    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 complexity of an image generation tool with no annotations and no output schema, the description is inadequate. It lacks details on behavioral traits, usage context, and output handling, leaving the agent with insufficient information to invoke it effectively beyond basic parameter passing.

    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 fully documents both parameters. The description adds no additional parameter semantics beyond what's in the schema, such as prompt formatting tips or output_dir constraints. Baseline 3 is appropriate when the schema handles parameter documentation.

    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 function with a specific verb ('Generate') and resource ('images'), specifying the model used ('Google Gemini Nano Banana Pro'). However, with no sibling tools mentioned, it cannot demonstrate differentiation from alternatives, though it's not required in this context.

    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 no guidance on when to use this tool versus alternatives, prerequisites, or contextual constraints. It simply states what the tool does without indicating appropriate scenarios or limitations.

    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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