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Gemini OCR MCP

Gemini OCR MCP

Local
github.com/WindoCAI & Agents8Updated 69w ago

About Gemini OCR MCP

Gemini OCR MCP is a FastMCP server for extracting text from images with the Google Gemini API. It accepts a local image file or a base64-encoded image.

Key features

  1. 01OCR from local image files
  2. 02OCR from base64 image data
  3. 03Text extraction using Google Gemini

Use cases

  1. 01Extract text from a scanned image file
  2. 02Read text from base64-encoded image input

MCP support

It exposes image OCR as MCP tools, accepting a file path or base64-encoded image and returning extracted text.

Configuration

Local server config is the same for every client. Copy it into your client’s MCP config file.

No install method listed yet

We haven’t collected a package or remote endpoint for this server. Follow the instructions in the project README.

Repository

Tools

No tool list yet

Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.

Details

From the project README

Gemini OCR MCP Server

This project provides a simple yet powerful OCR (Optical Character Recognition) service through a FastMCP server, leveraging the capabilities of the Google Gemini API. It allows you to extract text from images either by providing a file path or a base64 encoded string.

Objective

Extract the text from the following image:

CAPTCHA

and convert it to plain text, e.g., fbVk

Features

  • File-based OCR: Extract text directly from an image file on your local system.
  • Base64 OCR: Extract text from a base64 encoded image string.
  • Easy to Use: Exposes OCR functionality as simple tools in an MCP server.
  • Powered by Gemini: Utilizes Google's advanced Gemini models for high-accuracy text recognition.

Prerequisites

  • Python 3.8 or higher
  • A Google Gemini API Key. You can obtain one from Google AI Studio.

Setup and Installation

  1. Clone the repository:

    git clone https://github.com/WindoC/gemini-ocr-mcp
    cd gemini-ocr-mcp
    
  2. Create and activate a virtual environment:

    # Install uv standalone if needed
    
    ## On macOS and Linux.
    curl -LsSf https://astral.sh/uv/install.sh | sh
    
    ## On Windows.
    powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex"
    
  3. Install the required dependencies:

    uv sync
    

MCP Configuration Example

If you are running this as a server for a parent MCP application, you can configure it in your main MCP config.json.

Windows Example:

{
  "mcpServers": {
    "gemini-ocr-mcp": {
      "command": "uv",
      "args": [
        "--directory",
        "x:\\path\\to\\your\\project\\gemini-ocr-mcp",
        "run",
        "gemini-ocr-mcp.py"
      ],
      "env": {
        "GEMINI_MODEL": "gemini-2.5-flash-preview-05-20",
        "GEMINI_API_KEY": "YOUR_GEMINI_API_KEY"
      }
    }
  }
}

Linux/macOS Example:

{
  "mcpServers": {
    "gemini-ocr-mcp": {
      "command": "uv",
      "args": [
        "--directory",
        "/path/to/your/project/gemini-ocr-mcp",
        "run",
        "gemini-ocr-mcp.py"
      ],
      "env": {
        "GEMINI_MODEL": "gemini-2.5-flash-preview-05-20",
        "GEMINI_API_KEY": "YOUR_GEMINI_API_KEY"
      }
    }
  }
}

Note: Remember to replace the placeholder paths with the absolute path to your project directory.

Tools Provided

ocr_image_file

Performs OCR on a local image file.

  • Parameter: image_file (string): The absolute or relative path to the image file.
  • Returns: (string) The extracted text from the image.
ocr_image_base64

Performs OCR on a base64 encoded image.

  • Parameter: base64_image (string): The base64 encoded string of the image.
  • Returns: (string) The extracted text from the image.

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