Gemini OCR MCP Server
Provides OCR services powered by Google's Gemini API to extract text from images via file paths or base64 strings. It enables high-accuracy text recognition and CAPTCHA processing through simple MCP tools.
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.
推荐服务器
Baidu Map
百度地图核心API现已全面兼容MCP协议,是国内首家兼容MCP协议的地图服务商。
Playwright MCP Server
一个模型上下文协议服务器,它使大型语言模型能够通过结构化的可访问性快照与网页进行交互,而无需视觉模型或屏幕截图。
Magic Component Platform (MCP)
一个由人工智能驱动的工具,可以从自然语言描述生成现代化的用户界面组件,并与流行的集成开发环境(IDE)集成,从而简化用户界面开发流程。
Audiense Insights MCP Server
通过模型上下文协议启用与 Audiense Insights 账户的交互,从而促进营销洞察和受众数据的提取和分析,包括人口统计信息、行为和影响者互动。
VeyraX
一个单一的 MCP 工具,连接你所有喜爱的工具:Gmail、日历以及其他 40 多个工具。
graphlit-mcp-server
模型上下文协议 (MCP) 服务器实现了 MCP 客户端与 Graphlit 服务之间的集成。 除了网络爬取之外,还可以将任何内容(从 Slack 到 Gmail 再到播客订阅源)导入到 Graphlit 项目中,然后从 MCP 客户端检索相关内容。
Kagi MCP Server
一个 MCP 服务器,集成了 Kagi 搜索功能和 Claude AI,使 Claude 能够在回答需要最新信息的问题时执行实时网络搜索。
e2b-mcp-server
使用 MCP 通过 e2b 运行代码。
Neon MCP Server
用于与 Neon 管理 API 和数据库交互的 MCP 服务器
Exa MCP Server
模型上下文协议(MCP)服务器允许像 Claude 这样的 AI 助手使用 Exa AI 搜索 API 进行网络搜索。这种设置允许 AI 模型以安全和受控的方式获取实时的网络信息。