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Installation
Difficulty
IntermediateEstimated Time
10-20 minutes
Requirements
Python 3.7以上uv 最新版Installation
Installation
Prerequisites
Please specify required software and versions:Python: 3.7 or higher
uv: Latest version
Installation Steps
1. Clone Repository
bash
git clone https://github.com/cbinsights/cbi-mcp-server.git
cd cbi-mcp-server
2. Install Dependencies
bash
pip install -r requirements.txt
3. Set Environment Variables
Create a.env file and set the following environment variables:
bash
CBI_CLIENT_ID=your_client_id
CBI_CLIENT_SECRET=your_client_secret
CBI_MCP_PORT=8000
CBI_MCP_TIMEOUT=30
4. Start Server
bash
uv run server.py
Troubleshooting
Common Issues
Issue: Server won't start Solution: Check Python version and reinstall dependencies. Issue: Environment variables not set correctly Solution: Double-check the contents of the.env file.Configuration
Configuration
Basic Configuration
Set the following in the.env file:
bash
CBI_CLIENT_ID=your_client_id
CBI_CLIENT_SECRET=your_client_secret
CBI_MCP_PORT=8000
CBI_MCP_TIMEOUT=30
Advanced Configuration
Security Settings
Store API keys in environment variables and avoid hardcoding them in the code.
Set appropriate file access permissions.
Configuration Example
Basic Configuration
bash
CBI_CLIENT_ID=your_client_id
CBI_CLIENT_SECRET=your_client_secret
CBI_MCP_PORT=8000
CBI_MCP_TIMEOUT=30
Examples
Examples
Basic Usage
Using with Claude Desktop
1Verify MCP Server Startup
Open Claude Desktop and confirm that the configuration has been loaded correctly.
2Execute Basic Commands
Available tools from this MCP server:
- ChatCBI: Sends a message to the AI and returns the response.
Programmatic Usage
python
import requests
def call_chatcbi(message, chat_id=None):
response = requests.post(
'http://localhost:8000/chat',
json={
'message': message,
'chatID': chat_id
}
)
return response.json()
Usage example
result = call_chatcbi('Hello, AI!')
print(result)
Use Cases
Automating customer support through interactions with the AI chatbot.
Retrieving data analysis results from the AI to generate reports.
Providing real-time responses to user inquiries.
Building a content recommendation system based on related information.