MCP_Vincent

# Vincent `Vincent` is a repository for organizing and publishing reusable local `MCP plugin`. The current main product is `fastaicode-image-mcp`, which encapsulates the FastAICode compatible image interface into two tools that can be directly called by the MCP host: - `generate_image`: Generate images from text and automatically save them as local `PNG` files - `edit_image`: Edit images based on local images and automatically save them as local `PNG` files ## Current Repository Structure ```text . ├── CURRENT_TASK.md ├── DECISIONS.md ├── docs/ │ ├── plans/ │ └── requirements/ ├── fastaicode-image-mcp/ │ ├── .codex-plugin/ │ ├── .mcp.json │ ├── README.md │ ├── examples/ │ ├── server/ │ ├── skills/ │ └── tests/ └── outputs/ └── runtime/ └── vibe-sessions/ ``` ## How to Use MCP Structure The current plugin directory `fastaicode-image-mcp/` has four key structures: 1. `.codex-plugin/plugin.json` - Plugin metadata entry - Declare plugin name, Skill directory, and MCP server configuration file location 2. `.mcp.json` - Local server registration file read by MCP host - Currently uses `python3 ./fastaicode-image-mcp/server/mcp_server.py` by default 3. `server/` - `mcp_server.py`: MCP / JSON-RPC entry, handling `initialize`, `ping`, `tools/list`, `tools/call` - `client.py`: Send requests to FastAICode image interface and parse responses - `config.py`: Read `fastaicode-image-mcp.toml` and environment variables - `naming.py`: Output file naming - `models.py`: Response data model 4. `skills/fastaicode-image/SKILL.md` - Explain when to call `generate_image` - Explain when to call `edit_image` - Constrain the usage semantics of `1k / 2k / 4k / auto` ## Quick Start ```bash git clone git@github.com:Innnoa/MCP_Vincent.git cd Vincent/fastaicode-image-mcp python3 -m venv .venv source .venv/bin/activate python3 -m pip install --upgrade pip python3 -m pip install httpx pytest cp .env.example .env set -a source .env set +a pytest tests -v ``` The default configuration file is `fastaicode-image-mcp/fastaicode-image-mcp.toml`. If you want to change the interface address, default model, or default output directory, simply modify this file. ## Examples | Text-to-Image (`generate_image`) | Text-to-Image (`generate_image`) | |:---:|:---:| |  |  | | *orange fox logo* | *cute rubber duck* | For more parameter descriptions, see `fastaicode-image-mcp/README.md`. ## Deployment Method The current repository recommends "source code deployment": 1. Keep the `fastaicode-image-mcp/` directory structure unchanged 2. Install Python dependencies 3. Set `FASTAICODE_API_KEY` 4. Let MCP host point to `fastaicode-image-mcp/.mcp.json` If your MCP host starts the server using the current repository root directory as the working directory, the current `.mcp.json` can be used directly. If your MCP host does not start the server using the current repository root directory as the working directory, please change `./fastaicode-image-mcp/server/mcp_server.py` in `fastaicode-image-mcp/.mcp.json` to the absolute path on your local machine; such local path differences should only be modified on your local machine and not submitted back to GitHub. For more complete tool parameters, directory descriptions, and call examples, see `fastaicode-image-mcp/README.md`. ## Local Verification ```bash pytest fastaicode-image-mcp/tests -v ``` ## Current Limitations - Currently only supports local file input for image editing - Does not implement multi-image input, URL input, and mask - Availability of `4k` depends on the mapping in `fastaicode-image-mcp.toml`