Best Practice Workshop

A hands-on workshop demonstrating MCP (Model Context Protocol) best practices using Mastra. Build a "Customer Analytics" MCP server that showcases workflow-oriented tools, authentication patterns, and multi-system integration.

🎯 Workshop Goals

• Minimize surface area: 2 tools that handle many use cases
• Workflow-shaped tools: Capabilities (explore schema, run queries) vs endpoints (getUsers)
• Model compatibility: Test with multiple models, ensure consistent behavior
• Exploration: Let AI discover capabilities through resources
• Guardrails: Safe, deterministic, read-only by default

🏗️ What We'll Build

Customer Analytics MCP Server featuring:

• 📊 2 Tools: compute_account_health (multi-system workflow), run_sql (database queries)
• 📚 1 Resource: schema://main (discovery & exploration)
• 🔄 Workflow Patterns: External API integration, data fusion, scoring algorithms
• 🛡️ Built-in Safety: SELECT-only, implicit LIMIT, parsed queries, API rate limits
• 🔐 Authentication: Transparent role-based access control (admin/user/readonly)

🚀 Quick Start

Prerequisites

# Required
node >= 20.9.0
pnpm >= 8.0.0

# Optional: OpenAI API key for demo
export OPENAI_API_KEY="your-key-here"

Installation

# Clone and install
git clone <this-repo>
cd mcp-server-workshop-best-practices
pnpm install

# Terminal 1: Start HTTP MCP server with real auth
pnpm mcp-http-server

# Terminal 2: Run HTTP workshop demo
pnpm workshop-demo-http

# Server runs on http://localhost:3001 with endpoints:
# - /mcp (MCP endpoint with auth)
# - /health (health check)

🛠️ Technical Deep Dive

MCP Server Implementation

// src/mastra/mcp/server.ts
import { MCPServer } from "@mastra/mcp";
import { computeAccountHealthTool, runSqlTool } from "../tools";

const server = new MCPServer({
  name: "customer-analytics",
  version: "0.5.0",
  description: "Customer analytics MCP server with multi-system workflows",
  tools: {
    compute_account_health: computeAccountHealthTool,
    run_sql: runSqlTool,
  },
  resources: resourceHandlers,
});

Key Features:

• ✅ Multi-system workflows: compute_account_health combines database, external APIs, and business logic
• ✅ Resource-based discovery: Schema exploration via schema://main resource
• ✅ Transparent authentication: MCP-compliant auth context passed to tools
• ✅ Role-based access: admin/user/readonly permissions enforced per tool call
• ✅ Guardrails built-in: SELECT-only, auto-LIMIT, permission checking
• ✅ Error teaching: Structured, helpful error messages

Mastra Integration

// src/workshop-demo.ts
import { MCPClient } from "@mastra/mcp";
import { Agent } from "@mastra/core/agent";

const mcpClient = new MCPClient({
  servers: {
    customerAnalytics: {
      command: "pnpm",
      args: ["mcp-server"],
      env: { DEMO_API_KEY: "api_key_user_456" }, // Auth context
    },
  },
});

const agent = new Agent({
  name: "Customer Analytics Agent",
  model: openai("gpt-4o-mini"),
});

// Use MCP tools with agent
const response = await agent.generate(task, {
  toolsets: await mcpClient.getToolsets(),
});

HTTP Authentication (Production Pattern)

The workshop includes a production-ready HTTP server with real authentication:

// src/mastra/mcp/http-server.ts
import { MCPServer } from "@mastra/mcp";
import { server } from "./server";
import type { AuthInfo, DemoUserInfo } from "./utils";

// Authentication middleware
function authenticateRequest(req: http.IncomingMessage): AuthInfo | null {
  const authHeader = req.headers.authorization;

  // Support JWT Bearer tokens
  if (authHeader?.startsWith("Bearer ")) {
    return validateJWT(authHeader.slice(7));
  }

  // Support API keys
  if (authHeader?.startsWith("ApiKey ")) {
    return validateApiKey(authHeader.slice(7));
  }

  return null;
}

// HTTP request handler with MCP-compliant auth
const handleRequest = async (req, res) => {
  const authInfo = authenticateRequest(req);

  if (!authInfo) {
    res.writeHead(401, { "Content-Type": "application/json" });
    res.end(
      JSON.stringify({
        error: "Authentication required",
        message: "Please provide a valid Authorization header",
      }),
    );
    return;
  }

  // Attach MCP-compliant auth to request
  (req as any).auth = authInfo;

  await server.startHTTP({
    url: new URL(`http://localhost:${PORT}`),
    httpPath: "/mcp",
    req,
    res,
  });
};

Key Features:

• ✅ JWT & API Key Support: Multiple authentication methods
• ✅ MCP-Compliant Auth: Uses official AuthInfo type from MCP specification
• ✅ Real Auth Context: Authentication info passed to tools via options.extra.authInfo
• ✅ Session Management: Unique session IDs for each connection
• ✅ CORS Support: Web client compatibility
• ✅ Structured Errors: Clear auth failure responses

Test Credentials:

# Health check (no auth required)
curl localhost:3001/health

# MCP endpoint (requires auth)
curl -H "Authorization: ApiKey sk-user-987654321" \
     -H "Content-Type: application/json" \
     -d '{"jsonrpc":"2.0","method":"tools/list","id":1}' \
     localhost:3001/mcp

# Available credentials:
# API Keys: sk-admin-123456789, sk-user-987654321, sk-readonly-555666777
# JWT Tokens: eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.admin, eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.user

🛠️ Tools Overview

1. compute_account_health - Multi-System Workflow

Purpose: Demonstrates complex workflow patterns that combine multiple data sources for business insights.

Data Flow:

Internal DB (orders) → External NPS API → External Support API → Risk Scoring → Actionable Insights

Key Features:

• Multi-source data fusion: Order history + NPS scores + support tickets
• Business logic: Configurable scoring weights (recency 30%, momentum 30%, satisfaction 25%, reliability 15%)
• Segmentation: Filter by customer value, activity patterns, risk levels
• External API simulation: Realistic delays, missing data, enterprise customer patterns
• Role-based limits: Readonly users limited to 10 accounts

Input Parameters:

• segment: "all" | "inactive" | "highValue"
• windowDays: Analysis period (default 90 days)
• limit: Maximum accounts to analyze (default 50)
• includeReasons: Include risk factor explanations

Output Structure:

{
  "accounts": [
    {
      "accountId": "1",
      "name": "Alice Johnson",
      "healthScore": 85,
      "tier": "good",
      "metrics": { "lastOrderDays": 5, "spendDeltaPct": 15.2, "nps": 72 },
      "reasons": []
    }
  ],
  "summary": {
    "totalAnalyzed": 20,
    "segmentBreakdown": { "good": 15, "watch": 3, "at_risk": 2 },
    "avgHealthScore": 75,
    "externalDataCoverage": { "npsAvailable": 18, "supportDataAvailable": 20 }
  }
}

2. run_sql - Database Query Tool

Purpose: Safe, controlled database access with automatic guardrails.

Key Features:

• SELECT-only validation
• Automatic LIMIT injection
• Role-based row limiting
• Permission checking based on query content

🧪 Testing & Validation

Model Compatibility Test

Run the same task with multiple models:

Task: "Analyze customer health for high-value accounts and show database schema"

Expected flow:
1. Schema resource → understand available data structure
2. compute_account_health → multi-system customer analysis
3. run_sql → supplementary database queries if needed
4. Valid JSON response with insights and recommendations

Testing Matrix:

• ✅ Right tool chosen? (compute_account_health for analysis, run_sql for queries)
• ✅ Args valid? (proper segment filtering, reasonable limits)
• ✅ Result shape? (structured health scores, actionable insights)
• ✅ Authentication? (role-based access control working)

Demo Workflows

1. Schema Exploration: "What data is available?" (via schema resource)
2. Customer Health Analysis: "Show me at-risk customers" (compute_account_health)
3. Database Queries: "Show me recent high-value orders" (run_sql)
4. Multi-system Integration: "Combine order data with support tickets" (workflow tool)
5. Guardrails Test: "Try to delete users" (safely fails with helpful error)
6. Authentication Test: Different access levels (admin/user/readonly)

📊 Best Practices Demonstrated

✅ Tool Design

• Capability-oriented: run_sql (workflow) vs getUserById (endpoint)
• Self-documenting: Descriptions include examples
• Composable: One general tool > many specific ones

✅ Safety & Reliability

• Read-only default: Only SELECT operations allowed
• Implicit limits: Auto-add LIMIT clauses
• Structured errors: "Only SELECT queries allowed" (teaches the model)

✅ Model Compatibility

• Cross-model testing: GPT-4, GPT-3.5, etc.
• Consistent behavior: Same tools, same args, same output shape
• Graceful degradation: Weaker models still succeed

✅ Exploration & Discovery

• Resource-driven: Schema exposed via MCP resources
• Progressive disclosure: Start simple, add complexity
• Documentation: Examples and usage notes included

🎮 Try It Yourself

Extend the Demo

Add a third tool to show workflow composition:

const cityReportTool = createTool({
  id: "make_city_report",
  description: "Generate a comprehensive city analysis report",
  execute: async ({ context }) => {
    // Internally runs 2-3 SQL queries
    // Returns: { city, userCount, totalSpend, avgOrderValue }[]
  },
});

Connect to Your Own Database

Replace the in-memory data with real database connections:

// Update src/mcp-server/server.ts
import Database from "better-sqlite3";
const db = new Database("path/to/your/database.db");

// or with PostgreSQL
import postgres from "postgres";
const sql = postgres("postgresql://...");

// Update executeQuery function to use real SQL

Test with More Models

Add models to the compatibility test:

// src/workshop-demo.ts
const MODELS_TO_TEST = [
  { name: "GPT-4o Mini", model: openai("gpt-4o-mini") },
  { name: "GPT-4o", model: openai("gpt-4o") },
  { name: "Claude Sonnet", model: anthropic("claude-3-sonnet-20240229") },
  // Add your preferred models
];

🎯 Key Takeaways

1. Tools are workflows, not APIs - Design for capabilities
2. Fewer is better - 2 general tools > 10 specific ones
3. Safety first - Guardrails built into every tool
4. Test compatibility - Multiple models, same behavior
5. Enable exploration - Resources help discovery

📚 Resources

• Mastra MCP Documentation
• Model Context Protocol Spec

🤝 Contributing

Found an issue or want to improve the workshop?

• Report bugs via GitHub issues
• Submit improvements via pull requests
• Share your workshop experiences

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Built with ❤️ using Mastra - The TypeScript AI Framework