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Case Study: Connecting Supabase MCP to Claude Sonnet and GPT-5

3 min read

Introduction

Developers are increasingly experimenting with multiple large language models (LLMs) to enhance application capabilities. This case study shows how to connect Supabase MCP to both Claude Sonnet and GPT-5, leveraging JuheAPI’s multi-LLM support.

Overview of Supabase MCP

Supabase MCP (Model Connection Protocol) enables seamless integration between Supabase services and external AI models. It provides:

  • Unified connection handling
  • Secure token management
  • Scalable request routing

Why Connect to Claude Sonnet and GPT-5

Claude Sonnet offers advanced natural reasoning and summarization, while GPT-5 delivers powerful generation and coding capabilities. Linking both allows:

  • Hybrid output strategies
  • Model fallback options
  • Comparative response testing

Understanding JuheAPI's Multi-LLM Support

JuheAPI MCP Servers (https://www.juheapi.com/mcp-servers) allow developers to:

  • Route queries to multiple LLMs
  • Standardize payload formats
  • Monitor multi-model performance

JuheAPI acts as a hub, translating client requests for various model APIs without custom per-model integration code.

Setting up Supabase MCP Connection

Prepare Environment

  • Ensure Supabase project is active
  • Install latest MCP SDK
  • Obtain JuheAPI credentials

Configure MCP Server

  • Define server endpoint from JuheAPI
  • Set authentication method (API key)
  • Assign routing configuration for models

Connect to JuheAPI

import { MCPClient } from 'supabase-mcp';
const client = new MCPClient({
  endpoint: 'https://mcp.juheapi.com',
  apiKey: process.env.JUHEAPI_KEY
});

Integrating Claude Sonnet with Supabase MCP

Authentication and Endpoints

  • Use model identifier claude-sonnet
  • Endpoint managed by JuheAPI; call via MCP client

Handling Responses

Retrieve consistent JSON responses, parse content:

const res = await client.query({
  model: 'claude-sonnet',
  prompt: 'Summarize user feedback'
});
console.log(res.output);

Integrating GPT-5 with Supabase MCP

API Calls

  • Use model identifier gpt-5
  • Adjust temperature and max tokens for creative generation

Fine-Tuning Workflow

Fine-tune by feeding MCP with context-rich prompts over sequential calls for persistent session handling.

const res = await client.query({
  model: 'gpt-5',
  prompt: 'Write a concise product spec'
});

Testing and Debugging

Monitoring Logs

  • Utilize Supabase edge function logs to capture latency
  • JuheAPI dashboard to view model-side metrics

Performance Metrics

Track:

  • Latency per model
  • Output token count
  • Error rates

Best Practices for Multi-LLM Deployments

  • Keep prompts consistent for comparability
  • Implement version control for prompt sets
  • Maintain fallback model logic

Conclusion

By connecting Supabase MCP with Claude Sonnet and GPT-5 via JuheAPI, developers can test, compare, and deploy advanced AI capabilities in real-world applications with minimal integration complexity.