Which integrations are available for this server?

Integrates with Google's Gemini AI models (including variants like gemini-2.5-pro-preview, gemini-1.5-pro, etc.) to provide AI capabilities with automatic fallback mechanisms between experimental and stable models.

How do I use Gemini MCP Server?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@Gemini MCP Server review this Python function for security issues and suggest improvements" That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

Council MCP Server

A Model Context Protocol (MCP) server that enables Claude to collaborate with multiple AI models via OpenRouter. Access 100+ models from Google, Anthropic, OpenAI, Meta, Mistral, and more.

Features

Multi-Model Support: Access 100+ models via OpenRouter (Gemini, GPT, Claude, Llama, Mistral, etc.)
Dynamic Model Discovery: List and filter available models by provider, capability, or pricing
Per-Request Model Override: Use different models for different tasks
Multiple Collaboration Tools: Code review, brainstorming, test generation, explanations
Response Caching: Automatic caching for repeated queries

Related MCP server: Gemini MCP Server

Quick Start

1. Prerequisites

2. Installation

# Clone the repository
git clone https://github.com/lbds137/council-mcp-server.git
cd council-mcp-server

# Install dependencies
pip install -r requirements.txt

# Copy and configure environment
cp .env.example .env
# Edit .env and add your OPENROUTER_API_KEY

3. Configuration

Edit .env to configure:

# Your OpenRouter API key (required)
OPENROUTER_API_KEY=sk-or-...

# Model configuration (optional - defaults shown)
COUNCIL_DEFAULT_MODEL=google/gemini-3-pro-preview
COUNCIL_CACHE_TTL=3600
COUNCIL_TIMEOUT=600000

4. Register with Claude

# Install to MCP location
./scripts/install.sh

# Or manually register
claude mcp add council python3 ~/.claude-mcp-servers/council/launcher.py

Available Tools

Core Tools

Tool	Description
`ask`	General questions and problem-solving assistance
`code_review`	Code review feedback (security, performance, best practices)
`brainstorm`	Collaborative brainstorming for architecture and design
`test_cases`	Generate comprehensive test scenarios
`explain`	Clear explanations of complex code or concepts
`synthesize_perspectives`	Combine multiple viewpoints into a coherent summary

Model Management

Tool	Description
`server_info`	Check server status and current model
`list_models`	List available models with filtering
`set_model`	Change the active model for subsequent requests

Model Override

All tools support an optional model parameter to use a specific model:

# Use Claude for code review
mcp__council__code_review(
    code="def hello(): print('world')",
    focus="security",
    model="anthropic/claude-3-opus"
)

# Use GPT-4 for brainstorming
mcp__council__brainstorm(
    topic="API design patterns",
    model="openai/gpt-4-turbo"
)

Popular Model Configurations

Google Gemini (Default)

COUNCIL_DEFAULT_MODEL=google/gemini-3-pro-preview

Anthropic Claude

COUNCIL_DEFAULT_MODEL=anthropic/claude-3.5-sonnet

OpenAI GPT-4

COUNCIL_DEFAULT_MODEL=openai/gpt-4-turbo

Meta Llama (Free)

COUNCIL_DEFAULT_MODEL=meta-llama/llama-3.3-70b-instruct:free

Development

Project Structure

council-mcp-server/
├── src/council/           # Main source code
│   ├── main.py           # CouncilMCPServer entry point
│   ├── manager.py        # ModelManager (OpenRouter)
│   ├── providers/        # LLM provider implementations
│   ├── discovery/        # Model discovery and filtering
│   ├── tools/            # MCP tool implementations
│   ├── core/             # Registry and orchestrator
│   └── services/         # Cache and memory
├── tests/                # Test suite
├── scripts/              # Installation scripts
├── server.py             # Bundled single-file server
├── launcher.py           # Launcher with venv support
├── CLAUDE.md            # Claude Code instructions
└── README.md            # This file

Running Tests

# Create virtual environment
python -m venv .venv
source .venv/bin/activate

# Install dependencies
pip install -r requirements.txt

# Run tests
pytest tests/ -v

Building the Bundle

# Generate single-file server.py
python scripts/bundler.py

# Deploy to MCP location
./scripts/install.sh

Updating

To update your local MCP installation after making changes:

./scripts/install.sh

Then restart Claude Desktop/Code.

Troubleshooting

Server not found

# Check registration
claude mcp list

# Re-register if needed
./scripts/install.sh

API Key Issues

# Verify environment variable
echo $OPENROUTER_API_KEY

# Test with list_models tool
mcp__council__list_models(limit=5)

Model Not Available

Use list_models to find available models:

mcp__council__list_models(provider="google")

Version History

v4.0.0: Council - Multi-model support via OpenRouter
v3.0.0: Modular architecture with bundler
v2.0.0: Dual-model support with fallback
v1.0.0: Initial Gemini integration

License

MIT License - see LICENSE file for details.

Acknowledgments

Built for Claude using the Model Context Protocol
Powered by OpenRouter for multi-model access

Gemini MCP Server