DeepView MCP is a Model Context Protocol server that enables IDEs like Cursor and Windsurf to analyze large codebases using Gemini 2.5 Pro's extensive context window.
Documentation
DeepView MCP
DeepView MCP is a Model Context Protocol server that enables IDEs like Cursor and Windsurf to analyze large codebases using Gemini's extensive context window.
Features
- Load an entire codebase from a single text file (e.g., created with tools like repomix)
- Query the codebase using Gemini's large context window
- Connect to IDEs that support the MCP protocol, like Cursor and Windsurf
- Configurable Gemini model selection via command-line arguments
Prerequisites
- Python 3.13+
- Gemini API key from Google AI Studio
Installation
Installing via Smithery
To install DeepView for Claude Desktop automatically via Smithery:
npx -y @smithery/cli install @ai-1st/deepview-mcp --client claudeUsing pip
pip install deepview-mcpUsage
Starting the Server
Note: you don't need to start the server manually. These parameters are configured in your MCP setup in your IDE (see below).
# Basic usage with default settings
deepview-mcp [path/to/codebase.txt]
# Specify a different Gemini model
deepview-mcp [path/to/codebase.txt] --model gemini-2.0-pro
# Change log level
deepview-mcp [path/to/codebase.txt] --log-level DEBUGThe codebase file parameter is optional. If not provided, you'll need to specify it when making queries.
Command-line Options
--model MODEL: Specify the Gemini model to use (default: gemini-2.0-flash-lite)--log-level {DEBUG,INFO,WARNING,ERROR,CRITICAL}: Set the logging level (default: INFO)
Using with an IDE (Cursor/Windsurf/...)
1. Open IDE settings
2. Navigate to the MCP configuration
3. Add a new MCP server with the following configuration:
{
"mcpServers": {
"deepview": {
"command": "/path/to/deepview-mcp",
"args": [],
"env": {
"GEMINI_API_KEY": "your_gemini_api_key"
}
}
}
}
Setting a codebase file is optional. If you are working with the same codebase, you can set the default codebase file using the following configuration:{
"mcpServers": {
"deepview": {
"command": "/path/to/deepview-mcp",
"args": ["/path/to/codebase.txt"],
"env": {
"GEMINI_API_KEY": "your_gemini_api_key"
}
}
}
}
Here's how to specify the Gemini version to use:{
"mcpServers": {
"deepview": {
"command": "/path/to/deepview-mcp",
"args": ["--model", "gemini-2.5-pro-exp-03-25"],
"env": {
"GEMINI_API_KEY": "your_gemini_api_key"
}
}
}
}
4. Reload MCP servers configuration
### Available Tools
The server provides one tool:
1. `deepview`: Ask a question about the codebase
- Required parameter: `question` - The question to ask about the codebase
- Optional parameter: `codebase_file` - Path to a codebase file to load before querying
## Preparing Your Codebase
DeepView MCP requires a single file containing your entire codebase. You can use [repomix](https://github.com/yamadashy/repomix) to prepare your codebase in an AI-friendly format.
### Using repomix
1. **Basic Usage**: Run repomix in your project directory to create a default output file:Make sure you're using Node.js 18.17.0 or higher
npx repomix
This will generate a `repomix-output.xml` file containing your codebase.
2. **Custom Configuration**: Create a configuration file to customize which files get packaged and the output format:npx repomix --init
This creates a `repomix.config.json` file that you can edit to:
- Include/exclude specific files or directories
- Change the output format (XML, JSON, TXT)
- Set the output filename
- Configure other packaging options
### Example repomix Configuration
Here's an example `repomix.config.json` file:{
"include": [
"**/*.py",
"**/*.js",
"**/*.ts",
"**/*.jsx",
"**/*.tsx"
],
"exclude": [
"node_modules/**",
"venv/**",
"/__pycache__/",
"/test/"
],
"output": {
"format": "xml",
"filename": "my-codebase.xml"
}
}
For more information on repomix, visit the [repomix GitHub repository](https://github.com/yamadashy/repomix).
## License
MIT
## Author
Dmitry Degtyarev (ddegtyarev@gmail.com)Similar MCP
Based on tags & features
Trending MCP
Most active this week