A lightweight MCP server bridging AI agents to Google's Gemini AI via official CLI
Config is the same across clients — only the file and path differ.
{
"mcpServers": {
"gemini-bridge": {
"env": {},
"args": [
"gemini-bridge"
],
"command": "uvx"
}
}
}Are you the author?
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A lightweight MCP (Model Context Protocol) server that enables AI coding assistants to interact with Google's Gemini AI through the official CLI. Works with Claude Code, Cursor, VS Code, and other MCP-compatible clients. Designed for simplicity, reliability, and seamless integration.
Run this in your terminal to verify the server starts. Then let us know if it worked — your result helps other developers.
npx -y '@google/gemini-cli' 2>&1 | head -1 && echo "✓ Server started successfully"
After testing, let us know if it worked:
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Gemini CLI: Remote Code Execution via workspace trust and tool allowlisting bypasses
# Summary Gemini CLI (`@google/gemini-cli`) and the `run-gemini-cli` GitHub Action are being updated to harden workspace trust and tool allowlisting, in particular when used in untrusted environments like GitHub Actions. This update introduces a breaking change to how non-interactive (headless) environments handle folder trust, which may impact existing CI/CD workflows under specific conditions. # Details Folder Trust in Headless Mode In previous versions, Gemini CLI running in CI environmen
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A lightweight MCP (Model Context Protocol) server that enables AI coding assistants to interact with Google's Gemini AI through the official CLI. Works with Claude Code, Cursor, VS Code, and other MCP-compatible clients. Designed for simplicity, reliability, and seamless integration.
mcp>=1.0.0 and Gemini CLIInstall Gemini CLI:
npm install -g @google/gemini-cli
Authenticate with Gemini:
gemini auth login
Verify installation:
gemini --version
🎯 Recommended: PyPI Installation
# Install from PyPI
pip install gemini-bridge
# Add to Claude Code with uvx (recommended)
claude mcp add gemini-bridge -s user -- uvx gemini-bridge
Alternative: From Source
# Clone the repository
git clone https://github.com/shelakh/gemini-bridge.git
cd gemini-bridge
# Build and install locally
uvx --from build pyproject-build
pip install dist/*.whl
# Add to Claude Code
claude mcp add gemini-bridge -s user -- uvx gemini-bridge
Development Installation
# Clone and install in development mode
git clone https://github.com/shelakh/gemini-bridge.git
cd gemini-bridge
pip install -e .
# Add to Claude Code (development)
claude mcp add gemini-bridge-dev -s user -- python -m src
Gemini Bridge works with any MCP-compatible AI coding assistant - the same server supports multiple clients through different configuration methods.
# Recommended installation
claude mcp add gemini-bridge -s user -- uvx gemini-bridge
# Development installation
claude mcp add gemini-bridge-dev -s user -- python -m src
Global Configuration (~/.cursor/mcp.json):
{
"mcpServers": {
"gemini-bridge": {
"command": "uvx",
"args": ["gemini-bridge"],
"env": {}
}
}
}
Project-Specific (.cursor/mcp.json in your project):
{
"mcpServers": {
"gemini-bridge": {
"command": "uvx",
"args": ["gemini-bridge"],
"env": {}
}
}
}
Go to: Settings → Cursor Settings → MCP → Add new global MCP server
Configuration (.vscode/mcp.json in your workspace):
{
"servers": {
"gemini-bridge": {
"type": "stdio",
"command": "uvx",
"args": ["gemini-bridge"]
}
}
}
Alternative: Through Extensions