DeepView MCP
About
Enables AI-powered code analysis by loading entire codebases into Gemini's large context window, allowing developers to query and understand complex repositories through natural language interactions.
Details
- Author
- ai-1st
- Repository
- Mitek99/deepview-mcp
- GitHub stars
- 15
- Downloads
- 3,330
- License
- MIT License
- Categories
- Developer Tools, Community, Other, Design, File Management, AI, Search, Frontend
Jump to
- 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
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
DeepView MCPCommand (node, npx, python, etc.)/path/to/deepview-mcpEnvironment-
GEMINI_API_KEY
your_gemini_api_key
Please refer to the README for specific instructions on how to obtain API keys or other required environment variables.
-
GEMINI_API_KEY
- Enable "Start Automatically" if you want the plugin to start when Highlight launches
From the repository
To install DeepView for Claude Desktop automatically via Smithery:
npx -y @smithery/cli install @ai-1st/deepview-mcp --client claude
deepview
Ask a question about the codebase. Required parameter: question (string) - The question to ask about the codebase. Optional parameter: codebase_file (string) - Path to a codebase file to load before querying.
Claude Desktop / Cursor
Paste into your MCP client config file to install this server.
{
"mcpServers": {
"deepview mcp": {
"env": {
"GEMINI_API_KEY": "your_gemini_api_key"
},
"args": [],
"command": "/path/to/deepview-mcp"
}
}
}Linux
{
"env": {
"GEMINI_API_KEY": "your_gemini_api_key"
},
"args": [],
"command": "/path/to/deepview-mcp"
}Macos
{
"env": {
"GEMINI_API_KEY": "your_gemini_api_key"
},
"args": [],
"command": "/path/to/deepview-mcp"
}Windows
{
"env": {
"GEMINI_API_KEY": "your_gemini_api_key"
},
"args": [],
"command": "/path/to/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.
- 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
- Python 3.13+
- Gemini API key fromGoogle AI Studio
To install DeepView for Claude Desktop automatically viaSmithery:
npx -y @smithery/cli install @ai-1st/deepview-mcp --client claude
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 DEBUG
The codebase file parameter is optional. If not provided, you'll need to specify it when making queries.
- --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)
- Open IDE settings
- Navigate to the MCP configuration
- 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" } } } }
- 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
DeepView MCP requires a single file containing your entire codebase. You can userepomixto prepare your codebase in an AI-friendly format.
- 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 arepomix-output.xmlfile containing your codebase.
- Custom Configuration: Create a configuration file to customize which files get packaged and the output format:
This creates arepomix.config.jsonfile 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
Here's an examplerepomix.config.jsonfile:
{ "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 therepomix GitHub repository.
Dmitry Degtyarev (ddegtyarev@gmail.com)
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