vevani/fast-meraki-mcp-server

Fast Meraki MCP Server

A high-performance FastMCP server that provides intelligent access to Cisco Meraki network management APIs through semantic tool discovery and AI-powered features.

🚀 Features

• 🧠 Semantic Tool Discovery: AI-powered tool selection based on intent
• 🛡️ Null-Safe Processing: Automatic handling of API inconsistencies
• 📊 MCP Resources: Read-only data access through resource URIs
• 💬 MCP Prompts: Pre-built prompts for common network tasks
• ⚡ Optimized Performance: Connection pooling, caching, and lazy loading
• ✅ FastMCP 2.10.6+: Full compliance with latest MCP standards

📋 Prerequisites

• Python 3.12+
• Meraki API Key with read permissions (Get one here)
• FastMCP 2.10.6+

🔧 Installation

1. Clone the repository:

git clone <repository-url>
cd fast-meraki-mcp-server

2. Install dependencies:

pip install -r requirements.txt

3. Configure environment:

cp .env.example .env
# Edit .env and add your Meraki API key

4. Run the server:

python meraki_mcp_server.py

The server will start on http://0.0.0.0:8000/mcp

🎯 Core Capabilities

Essential Tools (Always Available)

• list_organizations - List all Meraki organizations
• get_organization - Get organization details
• list_networks - List networks in organization
• get_network - Get network details
• list_devices - List devices (organization or network level)
• get_device - Get device details
• list_clients - List network clients
• list_inventory - List inventory devices

Semantic Discovery Tools

• discover_tools - Find tools relevant to your intent
• analyze_context - Get suggestions based on conversation
• health_check - Check server and API status

Resources (Read-Only Data)

• meraki://organizations - All organizations
• meraki://organizations/{org_id}/networks - Networks for an organization
• meraki://networks/{network_id}/devices - Devices in a network

Prompts (Task Templates)

• network_health_check - Generate network health analysis prompt
• troubleshooting_guide - Create troubleshooting instructions
• configuration_audit - Generate configuration review prompt

💻 Usage Examples

With Claude Desktop

Add to your Claude Desktop configuration:

{
  "servers": {
    "meraki": {
      "command": "python",
      "args": ["/path/to/fast-meraki-mcp-server/meraki_mcp_server.py"],
      "env": {
        "MERAKI_API_KEY": "your_api_key_here"
      }
    }
  }
}

With FastMCP Client

from fastmcp import Client

async def main():
    async with Client("http://localhost:8000/mcp") as client:
        # List available tools
        tools = await client.list_tools()
        
        # Discover relevant tools
        result = await client.call_tool(
            "discover_tools", 
            {"intent": "monitor network performance"}
        )
        
        # Get organizations
        orgs = await client.call_tool("list_organizations", {})

🏗️ Architecture

The server implements a clean, modular architecture:

MerakiMCPServer
├── FastMCP Integration (OpenAPI-based tools)
├── Middleware System (null-safe processing, logging)
├── Semantic Discovery (AI-powered tool selection)
├── Resources (URI-based data access)
└── Prompts (Reusable task templates)

Key Components

1. OpenAPI Integration: Automatically generates tools from Meraki OpenAPI spec
2. Middleware Pipeline: Processes requests/responses for null handling and logging
3. Optimized Discovery: Cached, lazy-loaded semantic analysis
4. Connection Pooling: Efficient HTTP client with connection reuse

⚙️ Configuration

Environment variables (.env file):

# Required
MERAKI_API_KEY=your_api_key_here

# Optional
MCP_SERVER_HOST=0.0.0.0
MCP_SERVER_PORT=8000
LOG_LEVEL=INFO
MAX_CONNECTIONS=10
REQUEST_TIMEOUT=30
DISCOVERY_CACHE_SIZE=100

🔍 Troubleshooting

Check Server Health

curl http://localhost:8000/health

Common Issues

API Key Issues:

• Ensure MERAKI_API_KEY is set in .env
• Verify key has read permissions at Meraki Dashboard

Connection Issues:

• Check network connectivity to api.meraki.com
• Verify firewall allows outbound HTTPS (port 443)

Performance Issues:

• Increase MAX_CONNECTIONS for higher throughput
• Adjust DISCOVERY_CACHE_SIZE for better discovery performance

🧪 Development

Running Tests

pytest tests/ -v

Code Quality

# Format code
black .

# Lint
flake8 .

# Type checking
mypy .

📈 Performance

• Startup Time: ~2 seconds
• Tool Discovery: ~100ms (cached)
• API Response: ~750ms average
• Memory Usage: ~60MB
• Connection Pool: 10 concurrent connections

🤝 Contributing

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit changes (git commit -m 'Add amazing feature')
4. Push to branch (git push origin feature/amazing-feature)
5. Open a Pull Request

📝 License

Apache License 2.0 - see file for details.

👤 Author

Vidyadhar Evani

• Email: vidyadhar.evani@gmail.com
• GitHub: @vevani

Built with FastMCP 2.10.6+ | Optimized for Production | Full MCP Compliance