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nanobanana-mcp

nanobanana-mcp

by DojoCodingLabs
OverviewSchemaSourceRelated ServersDiscussions(1)Admin
TypeScript
Remote

generate_image

Create custom images from text prompts using Google Gemini models. This tool generates new visual content based on descriptive input for creative projects and design needs.

Instructions

Generate a NEW image from a text prompt using Gemini. Use this ONLY when creating a completely new image, not when modifying an existing one.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "prompt": "<prompt>"
    },
    "name": "generate_image"
  }
}

Input Schema

NameRequiredDescriptionDefault
promptYesText prompt describing the NEW image to create from scratch (max 10,000 chars)

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

A4.1/5.0
Behavior3/5

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

No annotations are provided, so the description carries the full burden. It mentions the tool uses Gemini and creates new images, but lacks details on behavioral traits like rate limits, authentication needs, output format, or potential errors. The description doesn't contradict annotations (none exist), but it's insufficient for a mutation tool with no annotation coverage.

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 sentences with zero waste: the first states the purpose and method, the second provides crucial usage guidance. It's front-loaded with the core function and efficiently distinguishes from siblings without unnecessary elaboration.

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?

For a mutation tool (image generation) with no annotations and no output schema, the description is incomplete. It covers purpose and usage well but lacks behavioral context like what the output looks like, error conditions, or limitations. The schema handles the parameter, but overall completeness is moderate given the tool's complexity.

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 the single 'prompt' parameter with its type, description, and constraints. The description adds no additional parameter semantics beyond what's in the schema, such as prompt formatting tips or examples. Baseline 3 is appropriate when 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 action ('Generate a NEW image'), resource ('image'), and method ('from a text prompt using Gemini'). It explicitly distinguishes this tool from its siblings by specifying 'creating a completely new image, not when modifying an existing one,' which directly contrasts with 'continue_editing' and 'edit_image' tools.

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 provides explicit guidance on when to use this tool ('ONLY when creating a completely new image') and when not to use it ('not when modifying an existing one'), with clear alternatives implied by the sibling tool names ('continue_editing' and 'edit_image'). This gives the agent precise context for tool selection.

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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