MCP server for comprehensive GitHub repository analysis using Gemini AI
Project description
CodeGlance MCP Server
An MCP (Model Context Protocol) server that analyzes GitHub repositories using Gemini AI and generates comprehensive documentation — project overviews, architecture guides, file insights, and more.
Works with any MCP-compatible client: Claude Code, Claude Desktop, Cursor, Windsurf, etc.
Quick Start
1. Get a Gemini API Key
Get a free API key from Google AI Studio.
2. Install & Configure
Claude Code
claude mcp add codeglance -e GEMINI_API_KEY=your_key_here -- uvx codeglance-mcp
That's it. Verify with:
claude mcp list
Claude Desktop / Cursor / Other MCP Clients
Add to your MCP config file (claude_desktop_config.json, .cursor/mcp.json, etc.):
{
"mcpServers": {
"codeglance": {
"command": "uvx",
"args": ["codeglance-mcp"],
"env": {
"GEMINI_API_KEY": "your_gemini_api_key_here"
}
}
}
}
Alternative: Install via pip
pip install codeglance-mcp
Then configure your MCP client to run codeglance-mcp as the command instead of uvx codeglance-mcp.
What It Does
When you ask your AI assistant to analyze a repository, CodeGlance:
- Clones the repository (shallow clone for speed)
- Reads key files (README, package.json, config files, entry points)
- Sends the context to Gemini AI with specialized analysis prompts
- Generates 6 documentation files in
codeglance-analysis/guide/
Generated Documentation
| File | Description |
|---|---|
01-overview.md |
5-minute project overview |
02-tree.md |
Annotated directory structure |
03-file-insights.md |
Key files and their purposes |
04-architecture.md |
System architecture deep-dive |
05-quick-start.md |
Getting started guide |
06-master-analysis.md |
Comprehensive technical analysis |
MCP Tools
| Tool | Description |
|---|---|
analyze_repository |
Run full analysis on a GitHub repo |
get_repository_info |
Check if a repo is already cloned |
list_generated_guides |
List generated documentation files |
MCP Prompts
| Prompt | Description |
|---|---|
comprehensive_analysis |
Full analysis workflow |
quick_overview |
Fast overview only |
architecture_review |
Architecture-focused analysis |
security_audit |
Security-focused review |
Configuration
All settings can be customized via environment variables in your MCP config:
| Variable | Default | Description |
|---|---|---|
GEMINI_API_KEY |
(required) | Your Google Gemini API key |
MAX_FILE_SIZE |
5000 |
Max characters per file to analyze |
MAX_FILES_PER_ANALYSIS |
50 |
Max files to include in analysis |
TIMEOUT_SECONDS |
120 |
API request timeout |
MAX_CONCURRENT_REQUESTS |
3 |
Concurrent Gemini API calls |
CACHE_TTL_SECONDS |
3600 |
In-memory cache TTL |
Example with custom settings:
{
"mcpServers": {
"codeglance": {
"command": "uvx",
"args": ["codeglance-mcp"],
"env": {
"GEMINI_API_KEY": "your_key",
"TIMEOUT_SECONDS": "180",
"MAX_CONCURRENT_REQUESTS": "5"
}
}
}
}
Requirements
- Python 3.11+
- Git (for cloning repositories)
- A Gemini API key (free tier works)
Development
git clone https://github.com/harshil/codeglance-mcp.git
cd codeglance-mcp
uv sync
# Run locally
GEMINI_API_KEY=your_key uv run codeglance-mcp
License
MIT
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