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Case Study: Connecting GitHub MCP to Claude Sonnet and GPT-5 via JuheAPI

4 min read

Introduction

Integrating powerful language models directly into your GitHub workflow can boost productivity for code reviews, doc generation, and automation. This case study shows how to connect GitHub’s Model Context Protocol (MCP) to both Claude Sonnet and GPT-5 APIs through JuheAPI’s multi-LLM endpoints.

Understanding GitHub MCP

MCP Architecture Basics

  • MCP standardizes communication between GitHub and external AI services.
  • It uses secure, event-driven protocols to call model endpoints from workflows or apps.

Where Claude and GPT Fit In

  • Claude Sonnet: excels at summarization, reasoning in natural language.
  • GPT-5: strong at code generation, multiturn dialogues.

Why Use JuheAPI’s Multi-LLM Endpoints

One Integration, Many LLMs

  • JuheAPI offers a single MCP server that can proxy to multiple LLM APIs.
  • No need to maintain separate connectors.

Simplified Authentication

  • Unified credential handling via JuheAPI secret keys.
  • Reduces risk of key sprawl.

Official site: JuheAPI MCP Servers

Preparation

GitHub MCP Setup

  • Ensure GitHub repository has MCP enabled.
  • Update .mcp/config.yml with endpoint definitions.

JuheAPI Account & Credentials

  • Sign up with JuheAPI.
  • Generate API key for multi-LLM service.

Connecting GitHub MCP to Claude Sonnet API

Endpoint Setup

  1. In JuheAPI dashboard, choose Claude Sonnet.
  2. Configure model parameters (e.g., temperature, max tokens).
  3. Copy MCP endpoint URL.

Sample Request/Response

POST https://mcp.juheapi.com/gpt5
{
  "prompt": "Generate unit tests for the data parser",
  "max_tokens": 500
}
curl --location --request POST 'https://wisdom-gate.juheapi.com/v1/chat/completions' \
--header 'Authorization: YOUR_API_KEY' \
--header 'Content-Type: application/json' \
--header 'Accept: */*' \
--header 'Host: wisdom-gate.juheapi.com' \
--header 'Connection: keep-alive' \
--data-raw '{
    "model":"wisdom-ai-claude-sonnet-4",
    "messages": [
      {
        "role": "user",
        "content": "Generate unit tests for the data parser"
      }
    ]
}'
Response:
{
  "output": "Here are the Jest unit tests..."
}

Debugging Tips

  • Check MCP logs in GitHub Actions.
  • Use JuheAPI test console for direct calls.

Connecting GitHub MCP to GPT-5 API

Endpoint Setup

  1. In JuheAPI dashboard, select GPT-5.
  2. Adjust parameters for code-focused output.
  3. Copy endpoint into .mcp/config.yml.

Sample Request/Response

POST https://mcp.juheapi.com/gpt5
{
  "prompt": "Generate unit tests for the data parser",
  "max_tokens": 500
}
curl --location --request POST 'https://wisdom-gate.juheapi.com/v1/chat/completions' \
--header 'Authorization: YOUR_API_KEY' \
--header 'Content-Type: application/json' \
--header 'Accept: */*' \
--header 'Host: wisdom-gate.juheapi.com' \
--header 'Connection: keep-alive' \
--data-raw '{
    "model":"wisdom-ai-gpt5",
    "messages": [
      {
        "role": "user",
        "content": "Generate unit tests for the data parser"
      }
    ]
}'
Response:
{
  "output": "Here are the Jest unit tests..."
}

Rate Limit Considerations

  • GPT-5 may have lower throughput than Claude.
  • Monitor usage in JuheAPI dashboard.

Switching Between Claude and GPT in MCP

Endpoint Switching Logic

  • Use MCP routing rules to send text summarization to Claude, code requests to GPT.
  • Define request filters based on command keywords.

Practical Workflow in GitHub Actions

  • PR event triggers MCP job.
  • Job decides model route:
    • codegen -> Claude Sonnet 4
    • summary -> GPT-5

Example: Multi-LLM Pull Request Assistant

MCP Event Triggers

  • Triggered on PR opened or updated.

Claude for Context, GPT-5 for Drafting

  • Claude generates human-friendly summaries.
  • GPT-5 drafts test cases or pseudocode.

Combined Output Management

  • Merge outputs in PR comment.
  • Clearly label sections for reviewers.

Best Practices

Keep Secrets Secure

  • Store JuheAPI key in GitHub Secrets.

Handle Failures Gracefully

  • Implement retries and timeouts in MCP jobs.

Monitor Costs and Latency

  • Use JuheAPI analytics.
  • Optimize prompts to reduce token usage.

Conclusion

By routing both Claude Sonnet and GPT-5 through JuheAPI's multi-LLM MCP server, you get flexible AI capabilities in one streamlined GitHub integration. This setup simplifies maintenance, improves productivity, and gives developers the freedom to choose the right model for each task.