About Gemini Ocr MCP
Gemini Ocr MCP is an MCP server published by WindoC in the Files category: a FastMCP-based OCR server powered by Google Gemini. Handles both image file paths and base64‑encoded images to return extracted text. Easy to integrate into MCP workflows — just set your Gemini API key and model, run the server, and call `ocr_image_file` or `ocr_image_base64`.
The repository has 8 stars and 3 forks, with the last commit a year ago. Six months or more without a commit doesn't mean the server is broken, but check the open issues before depending on it in production.
Install
git clone https://github.com/WindoC/gemini-ocr-mcpThis server has no ConduID identity, so agent calls to it are not receipted. Pin the version you install and review the source before granting it credentials.
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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:

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
-
Clone the repository:
git clone https://github.com/WindoC/gemini-ocr-mcp cd gemini-ocr-mcp -
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" -
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.
README copy captured when this server was indexed; it may lag the repository. The original is authoritative.