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Nano Banana MCP Server

by mikeroussell
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TypeScript
Remote

Nano Banana MCP Server

An MCP (Model Context Protocol) server for Google's Nano Banana Pro (Gemini 3 Pro Image) and Nano Banana (Gemini 2.5 Flash Image) AI image generation models.

Features

  • 🎨 Text-to-Image Generation - Create images from natural language descriptions

  • ✏️ Image Editing - Modify existing images with text prompts

  • 🖼️ Multi-Image Composition - Combine up to 14 reference images

  • 📐 Flexible Aspect Ratios - 10 aspect ratio options (1:1, 16:9, 9:16, etc.)

  • 🔍 High Resolution - Up to 4K output with Nano Banana Pro

  • 🔤 Text Rendering - Generate images with accurate, legible text

  • 🌐 Google Search Grounding - Real-time information for current events/weather

Related MCP server: Nano Banana MCP

Prerequisites

Installation

From Source

# Clone or download this directory
cd nanobanana-mcp-server

# Install dependencies
npm install

# Build the TypeScript
npm run build

Configuration

Set your API key as an environment variable:

# Linux/macOS
export GEMINI_API_KEY="your-api-key-here"

# Windows (PowerShell)
$env:GEMINI_API_KEY="your-api-key-here"

# Windows (CMD)
set GEMINI_API_KEY=your-api-key-here

Usage

As stdio Server (for Claude Desktop, etc.)

npm start

Or directly:

node dist/index.js

As HTTP Server

TRANSPORT=http PORT=3000 npm start

The server will listen at http://localhost:3000/mcp.

Claude Desktop Configuration

Add to your Claude Desktop config (~/Library/Application Support/Claude/claude_desktop_config.json on macOS):

{
  "mcpServers": {
    "nanobanana": {
      "command": "node",
      "args": ["/path/to/nanobanana-mcp-server/dist/index.js"],
      "env": {
        "GEMINI_API_KEY": "your-api-key-here"
      }
    }
  }
}

Available Tools

nanobanana_generate_image

Generate images from text descriptions.

Parameters:

Name

Type

Required

Description

prompt

string

Yes

Text description of the image

model

string

No

Model ID (default: gemini-3-pro-image-preview)

aspect_ratio

string

No

Aspect ratio (e.g., 1:1, 16:9)

resolution

string

No

Resolution: 1K, 2K, 4K (Pro only)

use_google_search

boolean

No

Enable real-time info grounding (Pro only)

Example:

{
  "prompt": "A photorealistic portrait of an astronaut on Mars at sunset, dramatic lighting, 85mm lens",
  "model": "gemini-3-pro-image-preview",
  "aspect_ratio": "16:9",
  "resolution": "4K"
}

nanobanana_edit_image

Edit an existing image using text prompts.

Parameters:

Name

Type

Required

Description

prompt

string

Yes

Description of the edit

image_base64

string

Yes

Base64-encoded image data

image_mime_type

string

Yes

MIME type (e.g., image/png)

model

string

No

Model ID

aspect_ratio

string

No

Output aspect ratio

resolution

string

No

Output resolution (Pro only)

Example:

{
  "prompt": "Add a wizard hat to the cat",
  "image_base64": "<base64-image-data>",
  "image_mime_type": "image/png"
}

nanobanana_compose_images

Compose images using multiple reference images (Nano Banana Pro only).

Parameters:

Name

Type

Required

Description

prompt

string

Yes

Description of composition

images

array

Yes

Array of {base64, mime_type} objects (max 14)

aspect_ratio

string

No

Output aspect ratio

resolution

string

No

Output resolution

Example:

{
  "prompt": "Create a group photo of these 5 people at a beach party",
  "images": [
    {"base64": "<image1>", "mime_type": "image/jpeg"},
    {"base64": "<image2>", "mime_type": "image/jpeg"}
  ],
  "aspect_ratio": "16:9",
  "resolution": "2K"
}

nanobanana_list_models

List available models and their capabilities.

Parameters:

Name

Type

Required

Description

response_format

string

No

markdown (default) or json

Models

Nano Banana Pro (gemini-3-pro-image-preview)

Google's most advanced image generation model. Best for:

  • Professional asset production

  • Images with accurate text rendering

  • Complex compositions

  • High-resolution output (up to 4K)

  • Real-time information grounding

Nano Banana (gemini-2.5-flash-image)

Fast, low-latency image generation. Best for:

  • Quick experimentation

  • Batch processing

  • Iterative design

  • Cost-effective generation

Aspect Ratios

Ratio

Use Case

1:1

Square, social media posts

16:9

Landscape, presentations, YouTube thumbnails

9:16

Portrait, mobile screens, Stories

4:3

Classic photo format

3:2

DSLR photo format

21:9

Ultra-wide, cinematic

Tips for Best Results

Prompting

  1. Be Descriptive: Include details about style, lighting, composition, colors, and mood

  2. Use Photography Terms: For photorealistic images, mention camera angles, lens types, lighting setups

  3. Specify Style: "Oil painting", "3D render", "watercolor", "photorealistic", etc.

  4. Include Context: Describe the environment, atmosphere, and mood

Example Prompts

Photorealistic:

A photorealistic close-up portrait of an elderly Japanese ceramicist with 
deep wrinkles and a warm smile, inspecting a glazed tea bowl. Soft golden 
hour light from a window. Shot with 85mm portrait lens, bokeh background.

Stylized:

A kawaii-style sticker of a happy red panda wearing a bamboo hat, munching 
on a leaf. Bold outlines, cel-shading, vibrant colors. White background.

Logo:

Create a modern, minimalist logo for a coffee shop called 'The Daily Grind'. 
Clean, bold sans-serif font. Black and white. Circular design with a clever 
coffee bean element.

Error Handling

Common errors and solutions:

Error

Solution

"GEMINI_API_KEY is required"

Set the environment variable

Rate limit exceeded

Wait and retry, or reduce request frequency

Content policy violation

Modify prompt to comply with Google's policies

Invalid image format

Use supported formats: PNG, JPEG, GIF, WebP

Development

# Install dependencies
npm install

# Build TypeScript
npm run build

# Watch mode for development
npm run dev

License

MIT

Links

Available Tools

4 tools
nanobanana_compose_imagesCompose Multiple Images with Nano Banana ProA
Read-only

Compose new images using multiple reference images with Nano Banana Pro.

Use up to 14 reference images to:

  • Create group compositions

  • Transfer styles between images

  • Maintain character consistency across scenes

  • Combine objects from different images

Limits:

  • Up to 6 images of objects for high-fidelity inclusion

  • Up to 5 images of humans for character consistency

  • Total maximum: 14 images

Args:

  • prompt (string, required): Description of how to compose the images

  • images (array, required): Array of image objects with:

    • base64 (string): Base64-encoded image data

    • mime_type (string): Image MIME type

  • model (string): Must be Nano Banana Pro (gemini-3-pro-image-preview)

  • aspect_ratio (string): Output aspect ratio

  • resolution (string): Output resolution (1K, 2K, 4K)

Returns:

  • success (boolean): Whether composition succeeded

  • imageData (string): Base64-encoded composed image

  • mimeType (string): Image MIME type

  • text (string): Any accompanying text

  • error (string): Error message if failed

Examples:

  • "Create a group photo of these 5 people at a beach"

  • "Apply the style of the first image to the subject in the second"

  • "Combine these product images into a catalog layout"

ParametersJSON Schema
NameRequiredDescriptionDefault
promptYesText description of how to compose/combine the images. Describe the desired scene, style transfer, or composition.
imagesYesArray of images to use as references. Up to 14 images total: 6 objects and 5 humans for character consistency. Each image needs base64 data and mime_type.
modelNoModel to use. Multi-image composition requires Nano Banana Pro (gemini-3-pro-image-preview).gemini-3-pro-image-preview
aspect_ratioNoAspect ratio of the generated image. Options: 1:1, 2:3, 3:2, 3:4, 4:3, 4:5, 5:4, 9:16, 16:9, 21:9. Default: varies by prompt
resolutionNoResolution of the generated image (Nano Banana Pro only). Options: 1K, 2K, 4K. Note: Must use uppercase 'K'. Default: 1K

TDQS

A4.1/5.0
Behavior4/5

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

Annotations already provide readOnlyHint=true and destructiveHint=false, indicating a safe read operation. The description adds valuable behavioral context beyond annotations by specifying image limits (up to 14 total, with breakdowns for objects and humans), which helps the agent understand constraints. It doesn't mention rate limits, authentication needs, or processing time, but adds meaningful operational context.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured with clear sections (purpose, use cases, limits, args, returns, examples) and front-loaded key information. It's appropriately sized for a complex tool, though the 'Args' and 'Returns' sections could be more concise since they largely repeat schema information.

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?

Given the tool's complexity (5 parameters, image processing), the description provides good context with use cases, limits, and examples. While there's no output schema, the 'Returns' section adequately documents the response structure. The description could benefit from more guidance on prompt engineering or error handling, but covers most essential aspects.

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 thoroughly. The description adds minimal value beyond the schema by briefly mentioning the 'images' array structure and model requirement, but doesn't provide additional semantic context or usage examples for parameters beyond what's in the schema descriptions.

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 purpose with specific verbs ('compose new images', 'transfer styles', 'maintain character consistency', 'combine objects') and resources ('multiple reference images', 'Nano Banana Pro'). It distinguishes from sibling tools by focusing on multi-image composition rather than editing single images, generating from scratch, or listing 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 provides clear context for when to use this tool through the bullet points of use cases (group compositions, style transfer, character consistency, object combination) and the limits section specifying image type constraints. However, it doesn't explicitly state when NOT to use it or name specific alternatives among the sibling tools.

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

nanobanana_edit_imageEdit Image with Nano BananaA
Read-only

Edit an existing image using text prompts with Google's Nano Banana models.

Provide an image and describe your desired changes. The model will:

  • Add, remove, or modify elements

  • Change style, lighting, or colors

  • Adjust composition

  • Apply filters or effects

The model maintains the original image's style and context while applying changes.

Args:

  • prompt (string, required): Description of the edit to make

  • image_base64 (string, required): Base64-encoded image data (no data URI prefix)

  • image_mime_type (string, required): MIME type of the image (e.g., 'image/png', 'image/jpeg')

  • model (string): Model to use. Default: Nano Banana Pro

  • aspect_ratio (string): Output aspect ratio. Options: 1:1, 2:3, 3:2, etc.

  • resolution (string): Output resolution (Pro only). Options: 1K, 2K, 4K

Returns:

  • success (boolean): Whether editing succeeded

  • imageData (string): Base64-encoded edited image

  • mimeType (string): Image MIME type

  • text (string): Any accompanying text from the model

  • error (string): Error message if editing failed

Examples:

  • "Add a small wizard hat on the cat's head"

  • "Change the background to a sunset beach"

  • "Make this image look like a Van Gogh painting"

  • "Remove the person in the background"

Error Handling:

  • Returns error if image data is invalid

  • Returns error if MIME type is unsupported

  • Returns error if content policy violated

ParametersJSON Schema
NameRequiredDescriptionDefault
promptYesText description of the edit to make. Describe what to add, remove, or modify. Be specific about the desired changes.
image_base64YesBase64-encoded image data to edit. Do not include data URI prefix.
image_mime_typeYesMIME type of the image. Supported: image/png, image/jpeg, image/jpg, image/gif, image/webp
modelNoModel to use. 'gemini-3-pro-image-preview' (Nano Banana Pro) for best quality and features, 'gemini-2.5-flash-image' (Nano Banana) for faster generation. Default: Nano Banana Progemini-3-pro-image-preview
aspect_ratioNoAspect ratio of the generated image. Options: 1:1, 2:3, 3:2, 3:4, 4:3, 4:5, 5:4, 9:16, 16:9, 21:9. Default: varies by prompt
resolutionNoResolution of the generated image (Nano Banana Pro only). Options: 1K, 2K, 4K. Note: Must use uppercase 'K'. Default: 1K

TDQS

A4.2/5.0
Behavior4/5

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

The description adds valuable behavioral context beyond what annotations provide. While annotations indicate read-only and non-destructive operations, the description elaborates on what the model actually does (add/remove/modify elements, change style/lighting/colors, adjust composition, apply filters), maintains original style/context, and includes error handling details. This provides practical behavioral insight that annotations alone don't convey.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured with clear sections (purpose, capabilities, args, returns, examples, error handling) and front-loads the core functionality. While comprehensive, some sections like the detailed parameter documentation could be more concise given the schema already covers them thoroughly. Most sentences earn their place by adding value.

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?

Given the tool's complexity (image editing with AI models), the description provides excellent contextual completeness. It covers purpose, capabilities, parameters, return values, examples, and error handling. While there's no output schema, the 'Returns' section thoroughly documents the response structure. The combination of rich description and comprehensive annotations makes this highly complete.

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?

With 100% schema description coverage, the schema already thoroughly documents all parameters. The description's 'Args' section essentially repeats what's in the schema without adding significant additional semantic context. The baseline score of 3 is appropriate since the schema does the heavy lifting, though the description provides some clarification about default values and model capabilities.

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 purpose with specific verb ('Edit') and resource ('an existing image'), and distinguishes it from siblings by specifying it's for editing existing images (vs. generating new ones with nanobanana_generate_image or composing multiple images with nanobanana_compose_images). The opening sentence establishes this distinction immediately.

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 context for when to use this tool ('Edit an existing image using text prompts') and includes examples that illustrate appropriate use cases. However, it doesn't explicitly state when NOT to use it or mention specific alternatives among the sibling tools, which prevents a perfect score.

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

nanobanana_generate_imageGenerate Image with Nano BananaA
Read-only

Generate high-quality images from text descriptions using Google's Nano Banana models.

This tool creates images from natural language prompts. For best results, be descriptive about:

  • Subject and composition

  • Style (photorealistic, illustration, painting, etc.)

  • Lighting and atmosphere

  • Colors and mood

  • Camera angle and lens (for photorealistic images)

Args:

  • prompt (string, required): Text description of the image to generate

  • model (string): Model to use. Options:

    • 'gemini-3-pro-image-preview' (Nano Banana Pro) - Best quality, 4K, text rendering

    • 'gemini-2.5-flash-image' (Nano Banana) - Fast generation Default: Nano Banana Pro

  • aspect_ratio (string): Image aspect ratio. Options: 1:1, 2:3, 3:2, 3:4, 4:3, 4:5, 5:4, 9:16, 16:9, 21:9

  • resolution (string): Image resolution (Pro only). Options: 1K, 2K, 4K

  • use_google_search (boolean): Enable real-time information grounding (Pro only)

Returns:

  • success (boolean): Whether generation succeeded

  • imageData (string): Base64-encoded image data

  • mimeType (string): Image MIME type (usually image/png)

  • text (string): Any accompanying text from the model

  • error (string): Error message if generation failed

Examples:

  • "A photorealistic portrait of an astronaut on Mars at sunset"

  • "Kawaii-style sticker of a happy corgi with a transparent background"

  • "Minimalist logo for 'TechStart' in blue and white, modern sans-serif font"

Error Handling:

  • Returns error if GEMINI_API_KEY is not set

  • Returns error if API rate limit exceeded (try again later)

  • Returns error if content policy violated

ParametersJSON Schema
NameRequiredDescriptionDefault
promptYesText description of the image to generate. Be descriptive for better results. Include details about style, lighting, composition, colors, and mood.
modelNoModel to use. 'gemini-3-pro-image-preview' (Nano Banana Pro) for best quality and features, 'gemini-2.5-flash-image' (Nano Banana) for faster generation. Default: Nano Banana Progemini-3-pro-image-preview
aspect_ratioNoAspect ratio of the generated image. Options: 1:1, 2:3, 3:2, 3:4, 4:3, 4:5, 5:4, 9:16, 16:9, 21:9. Default: varies by prompt
resolutionNoResolution of the generated image (Nano Banana Pro only). Options: 1K, 2K, 4K. Note: Must use uppercase 'K'. Default: 1K
use_google_searchNoEnable Google Search grounding for real-time information (e.g., current weather, news). Only available with Nano Banana Pro. Default: false

TDQS

A3.9/5.0
Behavior4/5

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

Annotations already indicate read-only and non-destructive behavior, but the description adds valuable context beyond annotations: it explains error handling (API key requirements, rate limits, content policy), provides best practices for prompts, and notes model-specific features (e.g., resolution for Pro only). No contradiction with annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured with clear sections (overview, args, returns, examples, error handling) and front-loaded key information. It is appropriately sized but could be slightly more concise by integrating some schema details (e.g., parameter defaults) that are redundant.

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?

Given the tool's complexity (5 parameters, no output schema), the description is mostly complete: it covers purpose, parameters, returns, examples, and error handling. However, it lacks explicit guidance on when to use versus sibling tools, and some behavioral details (e.g., response time, cost implications) are not addressed, leaving minor gaps.

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 thoroughly. The description adds minimal extra semantics (e.g., 'For best results, be descriptive' for prompt, model options with quality/fast trade-offs), but does not significantly enhance understanding beyond the schema. Baseline 3 is appropriate given 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 clearly states the tool's purpose: 'Generate high-quality images from text descriptions using Google's Nano Banana models.' It specifies the verb ('generate'), resource ('images'), and distinguishes from siblings by focusing on generation rather than composition, editing, or listing models.

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 implied usage through examples and best practices (e.g., 'be descriptive about subject, style, lighting'), but does not explicitly state when to use this tool versus alternatives like nanobanana_edit_image or nanobanana_compose_images. No exclusions or prerequisites are mentioned beyond error handling notes.

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

nanobanana_list_modelsList Nano Banana ModelsA
Read-onlyIdempotent

List available Nano Banana image generation models and their capabilities.

Returns information about:

  • Model IDs for API calls

  • Model names and descriptions

  • Features and capabilities

  • Maximum resolution supported

Args:

  • response_format (string): Output format. Options: 'markdown' (default), 'json'

Returns:

  • models: Array of model information objects with id, name, description, features, maxResolution

ParametersJSON Schema
NameRequiredDescriptionDefault
response_formatNoOutput format: 'markdown' for human-readable or 'json' for machine-readable. Default: markdownmarkdown

TDQS

A4.2/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true, destructiveHint=false, idempotentHint=true, and openWorldHint=false, covering safety and idempotency. The description adds useful context about the return structure (array of model information objects with specific fields) and output format options, which goes beyond annotations. No contradiction with annotations.

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 well-structured and front-loaded with the core purpose, followed by bullet points detailing return information and a concise parameter/return section. Every sentence adds value without redundancy, making it efficient and easy to parse.

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?

Given the tool's low complexity (one optional parameter, read-only operation) and rich annotations, the description is mostly complete. It details return values since there is no output schema, but could slightly improve by mentioning if the list is paginated or static. Overall, it provides sufficient context for effective use.

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 'response_format' fully documented in the schema (including enum values and default). The description repeats some of this information but adds minimal extra meaning, such as noting 'markdown' for human-readable and 'json' for machine-readable, which is already implied in the schema. Baseline 3 is appropriate as the schema does the heavy lifting.

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 specific verb ('List') and resource ('available Nano Banana image generation models and their capabilities'), distinguishing it from sibling tools that compose, edit, or generate images. It explicitly identifies what information is returned, making the purpose unambiguous.

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 implies usage by listing models for reference before using generation tools, but does not explicitly state when to use it versus alternatives or any exclusions. It provides clear context as a reference tool, but lacks explicit guidance on when not to use it.

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

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. 4 tool updatesv1.0.0
    • First observednanobanana_compose_images
    • First observednanobanana_edit_image
    • First observednanobanana_generate_image
    • First observednanobanana_list_models
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TDQS

A4.2/5.0
Disambiguation5/5

Each tool has a distinct and well-defined purpose: compose_images combines multiple reference images, edit_image modifies a single image, generate_image creates from text, and list_models provides metadata. The descriptions clearly differentiate their scopes, with no overlap or ambiguity in functionality.

Naming Consistency5/5

All tool names follow a consistent snake_case pattern with the prefix 'nanobanana_' and a clear verb_noun structure (e.g., compose_images, edit_image, generate_image, list_models). This uniformity makes the set predictable and easy to navigate.

Tool Count5/5

With 4 tools, this server is well-scoped for image generation and editing tasks. Each tool serves a unique and essential function in the workflow, from listing models to creating, editing, and composing images, without being overly sparse or bloated.

Completeness4/5

The tool set covers core image operations: generation, editing, composition, and model listing. Minor gaps might include batch processing or advanced filtering, but the surface supports typical agent workflows effectively, with no dead ends in the image manipulation lifecycle.

Maintenance

ActivityInactive
ResponsivenessNo issues

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