MCP-VizAPI: Visual Data Extraction with VizAPI.ai

@IvanZidov

代码仓库

基本信息

分类

数据与分析

许可证

MIT license

传输方式

stdio

作者

IvanZidov

关于 MCP-VizAPI: Visual Data Extraction with VizAPI.ai

暂无概览

#vizapi#data-extraction #mcp-server

MCP-VizAPI: Visual Data Extraction with VizAPI.ai 配置

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",
        "."
      ]
    }
  }
}

MCP-VizAPI: Visual Data Extraction with VizAPI.ai 工具

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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。

概览

What is MCP-VizAPI: Visual Data Extraction with VizAPI.ai?

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.

How to use MCP-VizAPI: Visual Data Extraction with VizAPI.ai?

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.

Key features of MCP-VizAPI: Visual Data Extraction with VizAPI.ai

List all private extraction templates for the authenticated user.
Retrieve a specific extraction template by its ID.
Analyze a document or image to suggest extraction fields.
Extract structured data using a predefined template.
Check the operational health of the VizAPI.ai service.

Use cases of MCP-VizAPI: Visual Data Extraction with VizAPI.ai

Automating invoice or receipt data extraction from scanned images.
Extracting structured information from business documents using custom templates.
Integrating visual data extraction into AI agent workflows (e.g., Claude Desktop, n8n).
Prototyping extraction pipelines by suggesting fields for new document types.

常见问题

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.

How do I get a VizAPI API key?

Obtain your API key from the VizAPI dashboard at `https://app.vizapi.ai/dashboard`. Set it as `VIZAPI_API_KEY` in your `.env` file.

What transport protocols are supported?

The server supports both Server-Sent Events (SSE) and Standard I/O (stdio) transports, configurable via the `TRANSPORT` environment variable.

Can I run the server without Docker?

Yes. Install dependencies with `uv` and run `uv run src/main.py` directly. Ensure `VIZAPI_API_KEY` is set in your `.env` file.

How do I configure the server for SSE transport?

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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