https://github.com/extrawest/agent_with_multiple_mcp_servers
A modular system for creating AI agents using the Model Context Protocol (MCP). This framework allows connecting to multiple MCP servers simultaneously, aggregating tools, and executing queries using LangChain agents
https://github.com/extrawest/agent_with_multiple_mcp_servers
agent ai langchain mcp openai python
Last synced: about 2 months ago
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A modular system for creating AI agents using the Model Context Protocol (MCP). This framework allows connecting to multiple MCP servers simultaneously, aggregating tools, and executing queries using LangChain agents
- Host: GitHub
- URL: https://github.com/extrawest/agent_with_multiple_mcp_servers
- Owner: extrawest
- License: apache-2.0
- Created: 2025-06-11T08:56:36.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2025-06-13T17:15:29.000Z (about 1 year ago)
- Last Synced: 2025-06-20T07:02:49.387Z (12 months ago)
- Topics: agent, ai, langchain, mcp, openai, python
- Language: Python
- Homepage:
- Size: 21.5 KB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# 🤖 MCP Agent Framework
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[](https://opensource.org/licenses/Apache-2.0)
A modular system for creating AI agents using the Model Context Protocol (MCP). This framework allows connecting to multiple MCP servers simultaneously, aggregating tools, and executing queries using LangChain agents.
## 🌟 Features
- 🔌 **Multi-server Connection**: Connect to multiple MCP servers simultaneously
- 🛠️ **Tool Aggregation**: Automatically collect and combine tools from all connected servers
- 🤖 **LangChain Integration**: Create React agents using the LangChain library
- 🔄 **Asynchronous Processing**: Fully asynchronous code with proper resource management
- 🧠 **OpenAI Integration**: Seamless integration with OpenAI models for intelligent agent responses
- 📊 **Detailed Logging**: Comprehensive logging of all operational stages for debugging
- 🧩 **Modular Architecture**: Clear separation into client and agent components
## Demo
https://github.com/user-attachments/assets/6ea18497-79b7-4ed9-8bc0-d211a6f3efd9
https://github.com/user-attachments/assets/31aec298-4a60-430c-b8aa-e5c525ebce06
## 🚀 Quick Start
### 1. Installation
```bash
# Clone the repository
git clone https://github.com/extrawest/agent_with_multiple_mcp_servers.git
cd agent_with_multiple_mcp_servers
# Install dependencies
pip install -r requirements.txt
```
### 2. Configuration
Copy the example environment file and configure your settings:
```bash
cp .env.example .env
```
### 3. Start MCP Servers
Before running the agent, you need to start the MCP servers. The project includes two different servers:
#### Stocks Server
Provides financial data tools using yfinance:
```bash
# Start the stocks server on port 7860
python -m mcp_components.server.stocks_server
```
#### News Server
Provides news retrieval tools using NewsAPI:
```bash
# Start the news server on port 7861
python -m mcp_components.server.news_server
```
### 4. Run the Agent
Once the servers are running, you can start the agent:
```bash
# Run the agent with connections to both MCP servers
python run_agent.py
```
### 5. Cursor IDE Integration
You can add the MCP servers to Cursor IDE by adding the following configuration to your Cursor settings:
```json
{
"mcpServers": {
"stocks-mcp-server": {
"url": "http://127.0.0.1:7860/sse"
},
"news-mcp-server": {
"url": "http://127.0.0.1:7861/sse"
}
}
}
```
Make sure both servers are running before connecting through Cursor IDE.
## 📦 Project Structure
```
mcp_components/
├── agent/
│ └── agent.py # AgentRunner implementation for working with MCP servers
├── client/
│ └── client.py # Client for connecting to MCP servers
├── server/
│ ├── stocks_server.py # MCP server providing stock market tools
│ └── news_server.py # MCP server providing news retrieval tools
```
## 💻 Usage
### Direct MCP Client Usage
```python
from mcp_components.client.client import MCPClientWrapper
from typing import List, Any, Tuple
async def get_mcp_client(server_url: str) -> Tuple[MCPClientWrapper, List[Any]]:
client = MCPClientWrapper(server_url)
await client.connect()
tools = await client.load_tools()
return client, tools
```
## 🔧 Components
### AgentRunner
Class for creating and managing agents that can use tools from multiple MCP servers:
- `setup_with_multiple_mcp_servers()`: Connect to multiple MCP servers and aggregate tools
- `run_query()`: Execute a query using the agent
- `cleanup()`: Close all connections and free resources
### MCPClientWrapper
Class for connecting to MCP servers and loading tools:
- `connect()`: Establish a connection with an MCP server
- `load_tools()`: Load available tools from the server
- `close()`: Close the connection to the server
### MCP Servers
The framework includes two specialized MCP servers:
#### Stocks Server
- Provides financial data tools using yfinance
- Runs on port 7860 by default
- Tools:
- `fetch_stock_info`: Get general company information
- `fetch_quarterly_financials`: Get quarterly financial data
- `fetch_annual_financials`: Get annual financial data
#### News Server
- Provides news retrieval tools using NewsAPI
- Runs on port 7861 by default
- Tools:
- `fetch_news`: Get news articles related to a query
## 📋 Requirements
- Python 3.9+
- MCP 1.9.3+
- LangChain
- OpenAI API key (for using OpenAI models)
- NewsAPI key (for the news server)
- yfinance (for the stocks server)
## 🔍 Implementation Details
- **Asynchronous Context Managers**: Proper management of asynchronous resources using `AsyncExitStack`
- **Error Handling**: Robust exception handling at all levels
- **Connection Preservation**: Maintaining active connections for tool usage
- **Tool Aggregation**: Combining tools from different servers into a unified list
Developed by [extrawest](https://extrawest.com/). Software development company