MCP-VizAPI: Visual Data Extraction with VizAPI.ai
@IvanZidov
关于 MCP-VizAPI: Visual Data Extraction with VizAPI.ai
暂无概览
基本信息
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",
"."
]
}
}
}工具
未检测到工具
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.
FAQ from MCP-VizAPI: Visual Data Extraction with VizAPI.ai
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.
数据与分析 分类下的更多 MCP 服务器
MCP Deep Web Research Server (v0.3.0)
qpd-vEnhanced MCP server for deep web research
🎓 Semantic Scholar MCP Server
JackKuo666🔍 This project implements a Model Context Protocol (MCP) server for interacting with the Semantic Scholar API. It provides tools for searching papers, retrieving paper and author details, and fetching citations and references.
Google Analytics MCP Server
surendranbGoogle Analytics 4 data to AI agents, agentic workflows, and MCP clients. Give agents analysis-ready access to website traffic, user behavior, and performance data with schema discovery, server-side aggregation, and safe defaults that reduce data wrangling.
PubMed Analysis MCP Server
DarkroasterA PubMed MCP server.
arxiv-latex MCP Server
takashiishidaMCP server that uses arxiv-to-prompt to fetch and process arXiv LaTeX sources for precise interpretation of mathematical expressions in scientific papers.
评论