Skip to main content
Glama
enesjakozh
sarthakkimtani

Image Generation MCP Server

by sarthakkimtani
OverviewSchemaRelated ServersScoreDiscussions
Python
Remote

Image Generation MCP Server

A Model Context Protocol (MCP) server that enables seamless generation of high-quality images via Together AI. This server provides a standardized interface to specify image generation parameters.

Features

  • High-quality image generation powered by the Flux.1 Schnell model

  • Support for customizable dimensions (width and height)

  • Clear error handling for prompt validation and API issues

  • Easy integration with MCP-compatible clients

Related MCP server: MCP Replicate FLUX

Installation

Claude Desktop

  • On MacOS: ~/Library/Application\ Support/Claude/claude_desktop_config.json

  • On Windows: %APPDATA%/Claude/claude_desktop_config.json

{
  "mcpServers": {
    "image-gen": {
      "command": "uv",
      "args": ["--directory", "/ABSOLUTE/PATH/TO/image-gen/", "run", "image-gen"],
      "env": {
        "TOGETHER_AI_API_KEY": "<API KEY>"
      }
    }
  }
}

Available Tools

The server implements one tool:

generate_image

Generates an image based on the given textual prompt and optional dimensions.

Input Schema:

{
  "prompt": {
    "type": "string",
    "description": "A descriptive prompt for generating the image (e.g., 'a futuristic cityscape at sunset')"
  },
  "width": {
    "type": "integer",
    "description": "Width of the generated image in pixels (optional)"
  },
  "height": {
    "type": "integer",
    "description": "Height of the generated image in pixels (optional)"
  },
  "model": {
    "type": "string",
    "description": "The exact model name as it appears in Together AI. If incorrect, it will fallback to the default model (black-forest-labs/FLUX.1-schnell)."
  }
}

Prerequisites

  • Python 3.12 or higher

  • httpx

  • mcp

Contributing

Contributions are welcome! Please follow these steps to contribute:

  1. Fork the repository

  2. Create a new branch (feature/my-new-feature)

  3. Commit your changes

  4. Push the branch to your fork

  5. Open a Pull Request

For significant changes, please open an issue first to discuss your proposed changes.

License

This project is licensed under the MIT License. See the LICENSE file for details.

Available Tools

1 tool
generate_imageC

Generate an image based on the text prompt, model, and optional dimensions

ParametersJSON Schema
NameRequiredDescriptionDefault
heightNoOptional height for the image
modelYesThe exact model name as it appears in Together AI. If incorrect, it will fallback to the default model (black-forest-labs/FLUX.1-schnell).
promptYesThe text prompt for image generation
widthNoOptional width for the image

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 the action ('Generate an image') but lacks details on permissions, rate limits, output format, or error handling. For a generative tool with zero annotation coverage, this is insufficient.

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 a single, efficient sentence that front-loads the core purpose without unnecessary details. Every word contributes to understanding the tool's function, making it appropriately concise and well-structured.

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 complexity of image generation, lack of annotations, and no output schema, the description is incomplete. It doesn't address behavioral aspects like response format, errors, or constraints, leaving significant gaps for the agent to infer usage.

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 fully documents all parameters. The description adds minimal value by listing inputs ('text prompt, model, and optional dimensions') but doesn't provide additional context beyond what the schema already specifies, aligning with the baseline for high coverage.

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 based on the text prompt, model, and optional dimensions.' It specifies the verb ('Generate') and resource ('image'), and mentions key inputs. However, with no sibling tools, it doesn't need to differentiate from alternatives, so a 5 is not warranted.

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 lists inputs without context about appropriate scenarios or limitations, leaving the agent with minimal usage direction.

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

Install Server
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

TDQS

B3.1/5.0
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

A single tool inherently has perfect naming consistency, as there are no other tools to compare it against. The name 'generate_image' follows a clear verb_noun pattern.

Tool Count2/5

A single tool for an image generation server feels thin and limited in scope. While it covers the core functionality, it lacks related operations like listing models, retrieving generated images, or managing parameters, making it borderline too few for the domain.

Completeness2/5

The server is severely incomplete for image generation. It only offers generation without any supporting tools for model selection, image retrieval, history management, or parameter tuning, which are common in such domains, leading to potential dead ends for agents.

Maintenance

ActivityInactive
ResponsivenessSyncing

Resources

Unclaimed servers have limited discoverability.

Looking for Admin?

If you are the server author, to access and configure the admin panel.

Related MCP Connectors

Related MCP Servers

Appeared in Searches

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/sarthakkimtani/mcp-image-gen'

If you have feedback or need assistance with the MCP directory API, please join our Discord server