https://github.com/ai-1st/deepview-mcp
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.
https://github.com/ai-1st/deepview-mcp
Last synced: 15 days ago
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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.
- Host: GitHub
- URL: https://github.com/ai-1st/deepview-mcp
- Owner: ai-1st
- License: mit
- Created: 2025-03-26T09:19:05.000Z (21 days ago)
- Default Branch: master
- Last Pushed: 2025-03-26T11:13:31.000Z (21 days ago)
- Last Synced: 2025-03-26T11:21:37.078Z (21 days ago)
- Language: Python
- Size: 34.2 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- awesome-mcp-servers - DeepView MCP - 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. (Table of Contents / Developer Tools)
- awesome-mcp-servers - DeepView MCP - 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. (Table of Contents / Developer Tools)
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README
# 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.
[](https://badge.fury.io/py/deepview-mcp)
## 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](https://aistudio.google.com/)
## Installation
### Using pip
```bash
pip install deepview-mcp
```
## 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).
```bash
# 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.
### 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:
```json
{
"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:
```json
{
"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:
```json
{
"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:
```bash
# 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:
```bash
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:
```json
{
"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 ([email protected])