Agent Aggregator
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@Agent Aggregatorlist files in /tmp and analyze any Python code"
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.
Agent Aggregator
MCP Server that aggregates tools from multiple MCP servers, acting as a proxy to provide unified access to various AI agents and tools.
🎯 Features
Multi-Agent Aggregation: Connects to multiple MCP servers simultaneously
Unified Tool Interface: Exposes all tools through a single MCP interface
AI Model Integration: Each agent can have an associated AI model via OpenRouter
Dynamic Configuration: Supports runtime configuration of connected agents
Error Handling: Robust error handling and connection management
Modern Node.js: Built with ES modules and modern JavaScript features
OpenRouter Support: Integrated support for AI models through OpenRouter API
Related MCP server: MCPHub
📁 Project Structure
agent-aggregator/
├── src/
│ ├── index.js # Main MCP server entry point
│ ├── aggregator/
│ │ ├── AgentAggregator.js # Core aggregation logic
│ │ ├── MCPConnection.js # Individual MCP server connection
│ │ └── OpenRouterClient.js # OpenRouter API integration
│ ├── config/
│ │ └── ConfigLoader.js # Configuration management
│ └── mcp-servers/ # Custom MCP server implementations
│ ├── README.md # MCP servers documentation
│ └── qwen_mcp_server.py # Qwen AI MCP server
├── config/
│ └── agents.json # Agent configuration file
├── tests/
│ └── integration.test.js # Integration tests with real services
├── scripts/
│ └── test-server.js # Manual server testing script
└── docs/ # Documentation🚀 Quick Start
Installation
# Install globally from npm
npm install -g agent-aggregator
# Or clone the repository for development
git clone https://github.com/rnd-pro/agent-aggregator.git
cd agent-aggregator
# Install dependencies
npm installQuick Start with Cursor
Add to Cursor MCP configuration (
~/.cursor/mcp.json):
{
"mcpServers": {
"agent-aggregator": {
"command": "npx",
"args": ["agent-aggregator"],
"env": {
"OPENROUTER_API_KEY": "your-openrouter-api-key",
"NODE_ENV": "production"
}
}
}
}Set your OpenRouter API key:
Get key from https://openrouter.ai/
Replace
your-openrouter-api-keywith actual key
Restart Cursor and you'll have access to 14+ tools from connected MCP servers:
Filesystem operations
Code analysis tools
AI assistance tools
And more based on your configuration
Configuration
Edit config/agents.json to configure which MCP servers to connect to:
{
"agents": [
{
"name": "filesystem",
"type": "mcp",
"enabled": true,
"description": "File system operations server",
"connection": {
"command": "npx",
"args": ["-y", "@modelcontextprotocol/server-filesystem", "/tmp"],
"env": {}
},
"model": {
"provider": "openrouter",
"name": "qwen/qwen3-coder:free",
"apiKey": "${OPENROUTER_API_KEY}"
}
}
],
"aggregator": {
"timeout": 30000,
"retryAttempts": 3,
"retryDelay": 1000
},
"defaults": {
"model": {
"provider": "openrouter",
"name": "qwen/qwen3-coder:free",
"apiKey": "${OPENROUTER_API_KEY}",
"baseUrl": "https://openrouter.ai/api/v1"
}
}
}Environment Variables
Set up your OpenRouter API key:
# For current session
export OPENROUTER_API_KEY="sk-or-v1-your-actual-key-here"
# Or create .env file in project root:
echo "OPENROUTER_API_KEY=sk-or-v1-your-actual-key-here" > .env
# For permanent setup (add to ~/.bashrc or ~/.zshrc):
echo 'export OPENROUTER_API_KEY="sk-or-v1-your-actual-key-here"' >> ~/.zshrcImportant: Never commit your actual API key to version control!
Running
# Start the MCP server
npm start
# Test the server
npm run test:server
# Run integration tests
npm test
# Development mode with auto-reload
npm run dev🔧 Usage
As MCP Server
Add to your MCP client configuration (e.g., Cursor):
{
"mcpServers": {
"agent-aggregator": {
"command": "npx",
"args": ["agent-aggregator"]
}
}
}Supported MCP Servers
Currently configured to work with:
Filesystem:
@modelcontextprotocol/server-filesystem- File system operationsClaude Code MCP:
@kunihiros/claude-code-mcp- Claude Code wrapper
You can add any MCP server that supports the standard MCP protocol. Popular options include:
@modelcontextprotocol/server-github- GitHub API operations@modelcontextprotocol/server-memory- Memory management@modelcontextprotocol/server-fetch- HTTP requests and web fetching
📊 Architecture
┌─────────────────┐ ┌──────────────────┐ ┌─────────────────┐
│ MCP Client │────│ Agent Aggregator │────│ Filesystem │
│ (Cursor) │ │ (This Server) │ │ MCP Server │
└─────────────────┘ │ │ └─────────────────┘
│ │ ┌─────────────────┐
│ │────│ Qwen AI │
│ │ │ MCP Server │
│ │ └─────────────────┘
│ │ ┌─────────────────┐
│ │────│ Claude Code │
│ │ │ MCP Server │
│ │ └─────────────────┘
│ │ ┌─────────────────┐
│ │────│ OpenRouter │
│ │ │ AI Models │
└──────────────────┘ └─────────────────┘The Agent Aggregator:
Connects to multiple downstream MCP servers
Aggregates their tools into a unified list
Routes tool calls to the appropriate server
Provides AI model access via OpenRouter for each agent
Returns results back to the client
🤖 AI Model Integration
Each MCP server can have an associated AI model that runs via OpenRouter. The default model is qwen/qwen3-coder:free.
Custom Methods
The aggregator provides custom MCP methods for AI interactions:
custom/agents/list- List all available agents and their capabilitiescustom/model/generate- Generate text using an agent's modelcustom/model/chat- Send chat completion requestscustom/models/info- Get information about all modelscustom/status- Get detailed status of all connections
🔍 Debugging
If you encounter issues, you can inspect the MCP server:
# Debug with MCP inspector
npx @modelcontextprotocol/inspector node src/index.js🛠️ Development
For developers who want to extend or contribute:
Adding New MCP Servers
Add server configuration to
config/agents.jsonInstall the MCP server package
Test the connection
Contributing
Fork the repository
Create a feature branch
Test your changes
Submit a pull request
📝 Configuration Options
Agent Configuration
{
"name": "unique-agent-name",
"type": "mcp",
"enabled": true,
"description": "Agent description",
"connection": {
"command": "command-to-run",
"args": ["--arg1", "--arg2"],
"env": {
"ENV_VAR": "value"
}
},
"model": {
"provider": "openrouter",
"name": "qwen/qwen3-coder:free",
"apiKey": "${OPENROUTER_API_KEY}"
}
}Aggregator Configuration
{
"aggregator": {
"timeout": 30000, // Connection timeout in ms
"retryAttempts": 3, // Number of retry attempts
"retryDelay": 1000, // Delay between retries in ms
"concurrentConnections": 2 // Max concurrent connections
},
"defaults": {
"model": {
"provider": "openrouter",
"name": "qwen/qwen3-coder:free",
"apiKey": "${OPENROUTER_API_KEY}",
"baseUrl": "https://openrouter.ai/api/v1"
}
}
}Available Models
The system uses OpenRouter API which supports many models:
qwen/qwen3-coder:free(default) - Free Qwen 3 Coder modelopenai/gpt-4o-mini- OpenAI GPT-4o Minianthropic/claude-3.5-sonnet- Claude 3.5 Sonnetmeta-llama/llama-3.1-8b-instruct:free- Free Llama modelAnd many more - see OpenRouter Models
## 🔍 Troubleshooting
### Common Issues
1. **"Could not attach to MCP server"**
- Check that the MCP server package is installed
- Verify the command and arguments in configuration
- Ensure the server supports the MCP protocol
2. **"Connection timeout"**
- Increase timeout in aggregator configuration
- Check that the MCP server starts properly
- Verify network connectivity
3. **"Tool not found"**
- Ensure the downstream MCP server is connected
- Check tool name prefixing (format: `agent-name__tool-name`)
- Verify the tool exists in the downstream server
4. **"OpenRouter API error"**
- Verify your OPENROUTER_API_KEY is set correctly
- Check that you have credits/access to the specified model
- Ensure the model name is correct (e.g., `qwen/qwen3-coder:free`)
5. **"No AI model configured"**
- Add a `model` section to your agent configuration
- Ensure the model configuration includes provider, name, and apiKey
- Check that environment variables are properly expanded
### Debug Mode
Enable debug logging by setting environment variables:
```bash
DEBUG=1 npm start🤝 Contributing
Follow the established code style
Add tests for new functionality
Update documentation
Test with real MCP servers
📚 Links
npm Package - Install from npm registry
GitHub Repository - Source code and issues
OpenRouter API - Get your API key for AI models
📄 License
MIT License
This server cannot be installed
Maintenance
Resources
Unclaimed servers have limited discoverability.
Looking for Admin?
If you are the server author, to access and configure the admin panel.
Latest Blog Posts
- Your AI Chatbot Just Exposed Your CEO's Salary to an InternBy Om-Shree-0709 on .Agent IdentityMCP SecurityOAuth Delegation
- Why MCP Servers Need Execution Sandboxing (And Why Your Current Stack Isn't Enough)By Om-Shree-0709 on .Agentic AiPrompt InjectionWebAssembly
MCP directory API
We provide all the information about MCP servers via our MCP API.
curl -X GET 'https://glama.ai/api/mcp/v1/servers/rnd-pro/agent-aggregator'
If you have feedback or need assistance with the MCP directory API, please join our Discord server