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Nano Banana MCP Server (CLIProxyAPI Edition)

by ion-aluminium
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Python
Hybrid

Generate or edit images (Multi-Model: Flash & Pro)

generate_image
Read-only

Create new images or modify existing ones using natural language prompts. Supports multiple input modes including generation, multi-image composition, and editing via file paths or IDs.

Instructions

Generate new images or edit existing images using natural language instructions.

Supports multiple input modes:

  1. Pure generation: Just provide a prompt to create new images

  2. Multi-image conditioning: Provide up to 3 input images using input_image_path_1/2/3 parameters

  3. File ID editing: Edit previously uploaded images using Files API ID

  4. File path editing: Edit local images by providing single input image path

Automatically detects mode based on parameters or can be explicitly controlled. Input images are read from the local filesystem to avoid massive token usage. Returns both MCP image content blocks and structured JSON with metadata.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
promptYesClear, detailed image prompt. Include subject, composition, action, location, style, and any text to render. Use the aspect_ratio parameter to pin a specific canvas shape when needed.
nNoRequested image count (model may return fewer).
negative_promptNoThings to avoid (style, objects, text).
system_instructionNoOptional system tone/style guidance.
input_image_path_1NoPath to first input image for composition/conditioning
input_image_path_2NoPath to second input image for composition/conditioning
input_image_path_3NoPath to third input image for composition/conditioning
file_idNoFiles API file ID to use as input/edit source (e.g., 'files/abc123'). If provided, this takes precedence over input_image_path_* parameters for the primary input.
modeNoOperation mode: 'generate' for new image creation, 'edit' for modifying existing images. Auto-detected based on input parameters if not specified.auto
model_tierNoModel tier: 'flash' (speed, 1024px), 'pro' (quality, up to 4K), or 'auto' (smart selection). Default: 'auto' - automatically selects based on prompt quality/speed indicators.auto
resolutionNoOutput resolution: 'high', '4k', '2k', '1k'. 4K and 2K only available with 'pro' model. Default: 'high'.high
thinking_levelNoReasoning depth for Pro model: 'low' (faster), 'high' (better quality). Only applies to Pro model. Default: 'high'.high
enable_groundingNoEnable Google Search grounding for factual accuracy (Pro model only). Useful for real-world subjects. Default: true.
aspect_ratioNoOptional output aspect ratio (e.g., '16:9'). See docs for supported values: 1:1, 2:3, 3:2, 3:4, 4:3, 4:5, 5:4, 9:16, 16:9, 21:9.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.4/5.0
Behavior4/5

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

The description adds significant behavioral context beyond the annotations. While annotations indicate read-only and open-world hints, the description explains the tool's multi-model behavior (Flash & Pro), automatic mode detection, local filesystem reading to avoid token usage, and dual return format (MCP image blocks + JSON metadata). No contradiction with annotations exists.

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 perfectly structured and front-loaded with the core purpose, followed by numbered usage modes and important behavioral notes. Every sentence adds value with zero waste, making it easy to scan while providing comprehensive information in minimal space.

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 complex 14-parameter tool with no output schema, the description provides excellent context about usage modes, behavioral characteristics, and return formats. It covers the multi-model aspect, automatic detection logic, and practical implementation details. The only minor gap is not explicitly mentioning sibling tool relationships.

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 documents all 14 parameters thoroughly. The description adds some context about parameter interactions (e.g., automatic mode detection based on parameters) and practical usage notes, but doesn't provide substantial additional semantic information 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 ('generate new images or edit existing images') and distinguishes it from siblings by specifying it uses natural language instructions and multiple input modes. It goes beyond the title by detailing the dual functionality and multi-model support.

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

Usage Guidelines5/5

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

The description explicitly lists four distinct usage modes (pure generation, multi-image conditioning, File ID editing, file path editing) with clear guidance on when to use each based on parameter combinations. It also explains automatic mode detection and provides practical advice about local filesystem usage to avoid token issues.

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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