Skip to main content
Glama
fahmidme

nano-banana-mcp

Official
by fahmidme
OverviewSchemaRelated ServersScoreDiscussions
TypeScript
Remote

Server Quality Checklist

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

  • Latest release: v0.1.0

  • Disambiguation5/5

    The two tools are unambiguously distinct: one generates images, the other retrieves task status for async operations. There is zero overlap in their purposes.

    Naming Consistency5/5

    Both tools follow the same verb_noun pattern with a shared 'nano_banana_' prefix: 'generate_image' and 'get_task'. This is perfectly consistent and predictable.

    Tool Count4/5

    With only two tools, the count is minimal but fits the narrow purpose of image generation with async polling. It could feel slightly thin, but the scope is clear and each tool is essential.

    Completeness4/5

    The surface covers the core workflow of generating images and retrieving results. Minor gaps exist (e.g., no task cancellation or listing), but these are not critical for the primary use case.

  • Average 4.1/5 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 The Unlicense.

  • 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.

  • Add a glama.json file to provide metadata about your server.

  • If you are the author, simply .

    If the server belongs to an organization, first add glama.json to the root of your repository:

    {
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}

    Then . Browse examples.

  • Add related servers to improve discoverability.

How to sync the server with GitHub?

Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.

To manually sync the server, click the "Sync Server" button in the MCP server admin interface.

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 burden of behavioral disclosure. It reveals that the operation is a read-style 'get' and includes a warning against hammering the server, which is useful. However, it does not explain error behavior, rate limits, or what the response contains beyond 'status/results'.

    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 two concise sentences with the purpose front-loaded and a brief, valuable usage tip. No unnecessary words or repetition.

    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?

    For a simple polling tool with one parameter, the description covers the core purpose and usage context. It lacks explicit return value details, but the low complexity and clear focus mitigate this. It is adequate but not exhaustive.

    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 already fully describes the single parameter (taskId) with 100% coverage, so the description adds little beyond reinforcing that the ID comes from the generation tool. This matches the baseline for high schema 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 explicitly states the tool's verb ('Get') and resource ('status/results') and ties it to a specific source (tasks from nano_banana_generate_image), making its purpose unmistakable. It also distinguishes from the sibling tool by focusing on polling rather than 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 clearly indicates when to use the tool (when polling tasks with auto-task enabled) and provides a practical tip (wait between polls). It does not explicitly state when not to use it or discuss alternatives, but the sibling relationship is clear enough.

    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 provided, the description carries the full burden of behavioral disclosure. It discloses the important async polling behavior for 4K requests and GCS uploads, but does not cover auth requirements, rate limits, or the exact nature of the return value (beyond polling task). This is partial but not exhaustive transparency.

    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 three sentences, each earning its place: the first states the core purpose, the second provides parameter guidance, and the third discloses an edge-case behavioral trait. It is front-loaded and free of padding.

    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 high complexity (18 parameters, multiple reference input methods, GCS interactions) and no output schema, the description should explain more about return values and workflows. It mentions the polling task for 4K but does not describe what the default return payload looks like or how to sequence with nano_banana_get_task. This leaves gaps, so a score of 3 is appropriate.

    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?

    Schema description coverage is 100%, so baseline is 3. The description adds value by recommending referenceImagePaths or referenceImageUris over referenceImages (base64) and by explaining the implications of imageSize=4K. These details go beyond the schema field descriptions, helping the agent choose parameters wisely.

    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 function: 'Generate images with Gemini 3 Pro Image on Vertex AI and upload results to GCS.' It specifies the action (generate), the resource (Gemini 3 Pro Image on Vertex AI), and the downstream effect (upload to GCS), fully distinguishing it from the sibling tool nano_banana_get_task which retrieves tasks.

    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 provides clear usage context by advising 'Prefer referenceImagePaths or referenceImageUris to avoid base64' and explaining the async behavior for 4K imageSize requests. It does not explicitly mention when not to use the tool or name alternatives beyond the implicit sibling relationship, but the guidance is actionable and context-rich.

    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.

Our badge communicates server capabilities, safety, and installation instructions.

Card Badge

nano-banana-mcp MCP server

Copy to your README.md:

Score Badge

nano-banana-mcp MCP server

Copy to your README.md:

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/fahmidme/nano-banana-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server