DeepView MCP

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:

Using pip

Usage

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).

The 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:

Here's how to specify the Gemini version to use:

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 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:

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:

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:

For more information on repomix, visit the repomix GitHub repository.

License

MIT

Author

Dmitry Degtyarev (ddegtyarev@gmail.com)