paddle-ocr-mcp-server
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@paddle-ocr-mcp-serverextract the text from this screenshot: ~/Desktop/receipt.png"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
paddle-ocr-mcp-server
PaddleOCR PP-OCRv6 的 FastMCP server:把云端 PaddleOCR 识别能力封装为标准 MCP 工具,任何 MCP 客户端(Claude Code 等)都能通过协议直接调用图片/截图/扫描件里的文字识别。
特性
一个工具搞定 OCR:
paddle_ocr(image),传入本地路径或 URL,返回识别文本行 + 置信度(JSON)。零第三方依赖的客户端:
ocr_client.py仅用 Python 标准库(urllib / json / base64)实现「提交 → 轮询 → 拉取结果」全流程,可独立复用。可测:解析逻辑抽成纯函数
parse_jsonl,6 个单测覆盖正常 / 空输入 / 异常路径,不联网、不依赖 token。token 走环境变量:
PADDLEOCR_TOKEN(已写入~/.zshrc),密钥不进代码不进仓库。
Related MCP server: ocr-mcp
快速开始
cd ~/Code/paddle-ocr-mcp-server
python3 -m venv .venv
.venv/bin/pip install -e ".[dev]"
# token 从环境变量读取(若新 shell 未继承,先 source ~/.zshrc 或 export)运行 / 验证
# 1) 直接跑 server(stdio,Claude Code 默认传输)
.venv/bin/paddle-ocr-mcp
# 2) 单测(不联网、不依赖 token)
.venv/bin/python -m pytest -q
# 3) 用 MCP inspector 调试(可选)
npx @modelcontextprotocol/inspector .venv/bin/paddle-ocr-mcp接入 Claude Code
claude mcp add paddle-ocr -- .venv/bin/paddle-ocr-mcp之后在 Claude Code 里直接说「识别这张图 xxx.png 里的字」,模型会自动调用 paddle_ocr 工具。
工具
paddle_ocr(image: str) -> strimage:图片本地路径或 URL返回 JSON 数组
[{"text": "...", "confidence": 0.99}, ...]
项目结构
src/paddle_ocr_mcp/
├── ocr_client.py # OCR 客户端(提交/轮询/解析),零第三方依赖,纯函数可测
└── server.py # FastMCP server + @mcp.tool paddle_ocr
tests/ # ocr_client 纯函数单测(不联网)开发
.venv/bin/python -m pytest -v # 跑测试
.venv/bin/paddle-ocr-mcp --transport sse # 需要 HTTP 传输时This server cannot be installed
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