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Image Generator MCP Server

by luoshui-coder
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image-generator MCP Server

An mcp server that generates images based on image prompts

This is a TypeScript-based MCP server that implements image generation using OPENAI's dall-e-3 image generation model.

Features

Tools

  • generate_image - Generate an image for given prompt

    • Takes prompt as a required parameter

    • Takes imageName as a required parameter to save the generated image in a generated-images directory on your desktop

Related MCP server: DALL-E MCP Server

Development

Install dependencies:

npm install

Build the server:

npm run build

For development with auto-rebuild:

npm run watch

Installation

To use with Claude Desktop, add the server config:

On MacOS: ~/Library/Application Support/Claude/claude_desktop_config.json On Windows: %APPDATA%/Claude/claude_desktop_config.json

{
  "mcpServers": {
    "command": "image-generator",
      "env": {
        "OPENAI_API_KEY": "<your-openai-api-key>"
    }
  }
}

Make sure to replace <your-openai-api-key> with your actual OPENAI Api Key.

Debugging

Since MCP servers communicate over stdio, debugging can be challenging. We recommend using the MCP Inspector, which is available as a package script:

npm run inspector

The Inspector will provide a URL to access debugging tools in your browser.

Available Tools

1 tool
generate_imageC

Generate an image from a prompt.

ParametersJSON Schema
NameRequiredDescriptionDefault
promptYesA prompt detailing what image to generate.
imageNameYesThe filename for the image excluding any extensions.

TDQS

C2.9/5.0
Behavior2/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions generation but doesn't describe side effects (e.g., file creation, rate limits, permissions needed, or output format). For a tool that likely creates files, this lack of detail is a significant gap.

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 extremely concise with a single sentence that directly states the tool's function. It is front-loaded and wastes no words, making it easy to parse quickly. Every word earns its place.

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 tool's complexity (image generation likely involves file creation and AI processing), no annotations, and no output schema, the description is incomplete. It lacks details on behavioral traits, output handling, and usage context, leaving significant gaps for an AI agent to understand how to invoke it correctly.

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 both parameters ('prompt' and 'imageName') adequately. The description adds no additional meaning beyond what the schema provides, such as prompt formatting tips or filename conventions. Baseline 3 is appropriate when schema does the heavy lifting.

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 purpose with a specific verb ('generate') and resource ('image'), and specifies the input mechanism ('from a prompt'). It doesn't need sibling differentiation since there are no sibling tools. However, it could be more specific about the type of image generation (e.g., AI model, format).

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 constraints. It simply states what the tool does without context about appropriate use cases or limitations. With no sibling tools, this is less critical but still a gap.

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.

  1. 1 tool update
    • First observedgenerate_image
Deploy Server
One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

TDQS

C2.9/5.0

Scored across 1 tool

Disambiguation5/5

With only one tool, there is no possibility of confusion or overlap between tools. The tool's purpose is clearly defined and distinct by default.

Naming Consistency5/5

The single tool name follows a clear verb_noun pattern (generate_image). Since there is only one tool, consistency is inherently perfect with no deviations.

Tool Count2/5

A single tool is too few for a server named 'Image Generator MCP Server', which suggests a broader scope. This minimal set limits functionality and feels thin for image generation tasks that might benefit from variations or additional operations.

Completeness2/5

The tool surface is severely incomplete for image generation. It only offers generation from a prompt, with no obvious support for editing, resizing, style adjustments, or other common image operations, leading to significant gaps in agent workflows.

Resources

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