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luoshui-coder

Image Generator MCP Server

by luoshui-coder
OverviewSchemaRelated ServersScoreDiscussions
Remote

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

C2.7/5.0
Behavior1/5

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. However, it only states the basic action ('Generate an image') without mentioning any behavioral traits such as permissions required, rate limits, output format, error conditions, or whether the operation is idempotent. This is inadequate for a tool that likely involves external resources or processing.

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—a single sentence that directly states the tool's purpose without any unnecessary words. It is front-loaded and wastes no space, making it easy for an agent to parse quickly. This efficiency is commendable for such a straightforward tool.

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 lack of annotations and output schema, the description is incomplete. It does not explain what the tool returns (e.g., image data, URL, error messages), nor does it cover behavioral aspects like authentication or limitations. For a tool with no structured metadata, the description should provide more context to guide the agent effectively.

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 description coverage is 100%, with clear descriptions for both parameters ('prompt' and 'imageName'). The description does not add any additional meaning beyond what the schema provides, such as examples or constraints. Given the high schema coverage, the baseline score of 3 is appropriate, as the schema adequately documents the parameters.

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: 'Generate an image from a prompt.' It specifies the verb ('Generate') and resource ('image'), making it easy to understand what the tool does. However, since there are no sibling tools mentioned, it cannot demonstrate differentiation from alternatives, which prevents a perfect score.

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 contextual constraints. It simply states what the tool does without any usage instructions, leaving the agent to infer appropriate scenarios based on the tool name and parameters alone.

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. 1 tool update
    • First observedgenerate_image
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TDQS

C2.9/5.0
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.

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