What prerequisites are needed to run the server?
Python 3.12+ and a VizAPI.ai account with a valid API key are required. Docker is recommended for containerized deployment.
@IvanZidov
基本信息
暂无概览
使用下面的配置,将此服务器添加到你的 MCP 客户端。
{
"mcpServers": {
"vizapi-mcp-server": {
"command": "uv",
"args": [
"pip",
"install",
"-e",
"."
]
}
}
}工具列表暂未提供
当前收录暂时没有可展示的工具列表,不代表服务没有工具。请查阅项目文档了解支持的能力。
工具是从 README 中自动提取的。维护者可以在 ## Tools 标题下列出工具,即可填充这部分内容。
MCP-VizAPI: Visual Data Extraction with VizAPI.ai is an MCP server that integrates with VizAPI.ai to enable AI agents to extract structured data from images and documents. It is designed for developers and users of MCP-compatible clients who need automated visual data extraction via predefined templates or field suggestions.
Install with uv (Python 3.12+ required) by cloning the repo and running uv pip install -e ., or use Docker (docker build -t mcp/vizapi --build-arg PORT=8060 .). Configure a .env file with VIZAPI_API_KEY and select transport (TRANSPORT=sse or TRANSPORT=stdio). Run via uv run src/main.py or docker run --env-file .env -p 8060:8060 mcp/vizapi. Connect your MCP client (e.g., Claude Desktop, Windsurf) using the provided SSE URL or stdio command.
Python 3.12+ and a VizAPI.ai account with a valid API key are required. Docker is recommended for containerized deployment.
Obtain your API key from the VizAPI dashboard at `https://app.vizapi.ai/dashboard`. Set it as `VIZAPI_API_KEY` in your `.env` file.
The server supports both Server-Sent Events (SSE) and Standard I/O (stdio) transports, configurable via the `TRANSPORT` environment variable.
Yes. Install dependencies with `uv` and run `uv run src/main.py` directly. Ensure `VIZAPI_API_KEY` is set in your `.env` file.
Set `TRANSPORT=sse` in your `.env` file, optionally adjust `HOST` and `PORT` (default 8060), and start the server. Connect your MCP client to `http://<host>:<port>/sse`.
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