MCP Developer Resources

Complete technical documentation, API references, and integration guides to help you build powerful AI applications with Model Context Protocol.

🚀 Quick Start Guide 🔧 API Reference

Documentation Overview

Everything you need to integrate and work with MCP

🚀

Quick Start Guide

Get up and running with MCP in minutes. Step-by-step installation and basic configuration guide.

# Install MCP SDK
npm install @58com/mcp-sdk

# Initialize MCP client
import { MCPClient } from '@58com/mcp-sdk';
const client = new MCPClient({
  endpoint: 'https://api.58com.net/mcp',
  apiKey: 'your-api-key'
});

🏗️

Architecture Guide

Understand MCP's core architecture, design patterns, and best practices for scalable AI system integration.

✅ Protocol Overview
✅ Message Flow Patterns
✅ Context Management
✅ Error Handling

🔧

API Reference

Complete API documentation with endpoints, parameters, response formats, and authentication methods.

POST /api/v1/context/create
{
  "name": "chat-session",
  "metadata": {
    "user_id": "12345",
    "session_type": "conversation"
  },
  "ttl": 3600
}

📦

SDK Documentation

Language-specific SDKs with detailed documentation, examples, and best practices.

✅ JavaScript/TypeScript SDK
✅ Python SDK
✅ Go SDK
✅ Java SDK

💡

Integration Examples

Real-world integration examples and use cases demonstrating MCP implementation patterns.

// Context sharing example
const context = await client.createContext({
  name: 'ml-pipeline',
  data: {
    model_version: 'v2.1.0',
    training_data: dataset
  }
});

await model1.shareContext(context);
const result = await model2.processWithContext(context);

🔐

Security & Authentication

Comprehensive security documentation covering authentication, encryption, and access control.

✅ OAuth 2.0 Integration
✅ JWT Token Management
✅ Rate Limiting
✅ Audit Logging

🚢

Deployment Guide

Production deployment strategies, scaling considerations, and monitoring best practices.

# Docker deployment
docker run -d \
  --name mcp-gateway \
  -p 8080:8080 \
  -e MCP_CONFIG=/config/mcp.yaml \
  58com/mcp-gateway:latest

🔍

Troubleshooting

Common issues, debugging techniques, and solutions for MCP integration problems.

✅ Connection Issues
✅ Performance Optimization
✅ Error Code Reference
✅ Debug Tools

👥

Community Resources

Community-driven resources, tutorials, plugins, and contributions from the MCP ecosystem.

✅ Community Plugins
✅ Video Tutorials
✅ Blog Articles
✅ Discussion Forums

Code Examples

Practical examples to accelerate your development

🐍 Python Integration

from mcp_sdk import MCPClient

# Initialize client
client = MCPClient(
    endpoint="https://api.58com.net/mcp",
    api_key="your-api-key"
)

# Create context
context = client.create_context(
    name="ai-pipeline",
    metadata={"model": "gpt-4", "task": "classification"}
)

# Share context between models
model_a = client.get_model("model-a")
model_b = client.get_model("model-b")

result_a = model_a.process(data, context)
result_b = model_b.process(result_a, context)

🟨 JavaScript/Node.js

import { MCPClient } from '@58com/mcp-sdk';

const client = new MCPClient({
  endpoint: 'https://api.58com.net/mcp',
  apiKey: process.env.MCP_API_KEY
});

// Async/await pattern
async function aiWorkflow() {
  const context = await client.createContext({
    name: 'user-session',
    ttl: 3600
  });

  const models = await Promise.all([
    client.getModel('nlp-model'),
    client.getModel('ml-classifier')
  ]);

  const [nlpResult, classifyResult] = await Promise.all([
    models[0].process(inputText, context),
    models[1].classify(inputText, context)
  ]);

  return { nlpResult, classifyResult };
}