Introduction
GitHub MCP (Model Context Protocol) bridges local developer tools with powerful cloud AI models. For developers wanting to integrate large language model (LLM) capabilities into workflows, MCP simplifies orchestration.
This tutorial offers practical steps for connecting GitHub MCP to LLM APIs provided by JuheAPI.
Understanding GitHub MCP
What MCP Does in Dev Workflows
- Acts as a standardized bridge between development environments and AI endpoints
- Enables consistent context sharing between tools and LLMs
Key Benefits for LLM Integration
- Unified API call structure
- Simple configuration management
- Enhanced reproducibility across projects
Connecting MCP to LLM APIs
JuheAPI Overview
JuheAPI offers multiple LLM endpoints for text generation, summarization, and more.
Generating API Keys
- Sign in to JuheAPI
- Navigate to API Dashboard
- Create and label a new API key for your project
MCP Configuration for JuheAPI
- Locate your MCP config YAML (typically in
.mcp/config.yml) - Add JuheAPI endpoint details and keys
Step-by-Step Integration Walkthrough
Project Preparation
- Ensure MCP server is running
- Keep your JuheAPI key ready
Practical Example: JuheAPI Text Generation
Model Selection
- Choose from available models in JuheAPI dashboard
Sending Prompts via MCP
mcp call juheapi "Write a 50-word travel blog intro for Kyoto"
Review and refine prompt for best results
Advanced Tips and Troubleshooting
Connection Errors
- Check endpoint URL formatting
- Ensure no firewall blocks MCP
Rate Limit Handling
- JuheAPI applies request limits—log usage and cache common responses
- Implement exponential backoff for retries
Best Practices for Production
- Use environment variables for API keys
- Log all requests/responses for audit
- Monitor latency and errors continuously