RapidOCR MCP Server
About
A MCP server based on RapidOCR, providing an easy-to-use OCR interface.
Details
- Author
- z4none
- GitHub stars
- 5
- Downloads
- 383
- Categories
- Other
Jump to
- Exposes two OCR methods: ocr_by_content and ocr_by_path.
- Accepts base64-encoded image data or a local file path.
- Returns a list of TextContent objects with recognized text.
- Easy setup via uvx with no extra configuration.
- Designed to integrate seamlessly with MCP-compatible clients.
Setting up with Highlight
This MCP is not yet compatible with Highlight’s one-click setup. However, you can still use it with Highlight by following these steps:
- Download and install Highlight from highlightai.com/download
- Navigate to the plugins tab and select "Add Custom Plugin"
-
Configure the plugin with the settings below
Plugin Name
RapidOCR MCP ServerCommand (node, npx, python, etc.)Please refer to the README for specific instructions on how to obtain API keys or other required environment variables.
- Enable "Start Automatically" if you want the plugin to start when Highlight launches
From the repository
Run the server with uvx run rapidocr-mcp. Configure it in your MCP client using the provided JSON block with command: "uvx" and args: ["rapidocr-mcp"]. No additional environment variables are needed.
Claude Desktop / Cursor
Paste into your MCP client config file to install this server.
{
"mcpServers": {
"rapidocr mcp server": {
"rapidocr-mcp": {
"command": "uvx",
"args": [
"run",
"rapidocr-mcp"
]
}
}
}
}McpServers
{
"rapidocr-mcp": {
"command": "uvx",
"args": [
"run",
"rapidocr-mcp"
]
}
}RapidOCR MCP Server
A MCP server based on RapidOCR, providing an easy-to-use OCR interface.
Usage
uvx run rapidocr-mcp
MCP config
"rapidocr-mcp": {
"command": "uvx",
"args": [
"rapidocr-mcp"
],
"env": {}
}
Screenshots
Available Methods
ocr_by_content
Perform OCR on an image content. Args: base64_data (str): The base64 encoded image content. Returns: List[TextContent]: A list of text content.
ocr_by_path
Perform OCR on an image file. Args: path (str): The path to the image file. Returns: List[TextContent]: A list of text content.
remark
Currently, large language models are becoming increasingly powerful and generally possess multimodal capabilities, enabling them to recognize text in images without the need for external OCR tools.
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