What are the system requirements?
Python 3.13+, a locally running ComfyUI (default http://localhost:8188), the `uv` package manager, and a locally running ollama with a vision model for captioning.
@rerofumi
Basic information
LLM MCP server for image generation with ComfyUI
Add this server to your MCP-compatible client using the configuration below.
{
"mcpServers": {
"fm-mcp-comfyui-bridge": {
"command": "uv",
"args": [
"pip",
"install",
"-e",
"."
]
}
}
}Tool list not available yet
This listing does not currently have a tool list to display. This does not mean the server has no tools; check its documentation for supported capabilities.
We auto-extract tools from the README. The maintainer can list them under a ## Tools heading to populate this section.
fm-mcp-comfyui-bridge is an MCP server implementation that provides image generation, captioning, and tag parsing by accessing the ComfyUI API. It is designed for developers using AI agent tools who need to interact with a local ComfyUI instance.
Install using uv (pip install -e .), then configure the MCP client with the command uv --directory <path> run fm-mcp-comfyui-bridge. Set the ComfyUI endpoint (default http://localhost:8188), create a config.yaml for checkpoint and LoRA settings, and an ollama.yaml for vision model. Use the provided tools generate_picture, get_picture, get_caption, and get_tag via the MCP interface.
Python 3.13+, a locally running ComfyUI (default http://localhost:8188), the `uv` package manager, and a locally running ollama with a vision model for captioning.
Clone the repository, run `uv pip install -e .`, then configure your MCP client with the command `uv --directory <path> run fm-mcp-comfyui-bridge`. Optionally copy the sample config file and edit `config.yaml` for your model and LoRA settings.
Four tools: `generate_picture` (generate image from prompt), `get_picture` (get PNG binary), `get_caption` (get text caption), and `get_tag` (get WD1.4 tags). API resources include `info://about`, `help://tools`, and `docs://{topic}`.
Place an API-format workflow JSON in `src/fm_mcp_comfyui_bridge/config/workflow/` and create a `custom.yaml` in the config directory. If `custom.yaml` exists, the custom workflow runs; otherwise the default workflow is used.
The tag analysis part uses source code and model data from SmilingWolf’s wd-tagger. The model data is downloaded on first execution.

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