What Is MCP (Model Context Protocol)?

MCP — Model Context Protocol — is an open standard created by Anthropic that lets AI models connect to external tools, data sources, and services. Think of it as a "USB port" for AI: a universal way to plug in new capabilities.

Without MCP, AI assistants are limited to what they know from training data. With MCP, they can:

  • Query your database in real-time
  • Call external APIs (Slack, GitHub, Jira, etc.)
  • Read and write files beyond the local filesystem
  • Access private knowledge bases and documentation
  • Control external services (deploy, monitor, alert)

How MCP Works

The Architecture

MCP uses a client-server model: MCP Client (your AI tool — Claude Code, Cursor, VS Code):

  • Discovers available MCP servers
  • Sends requests to servers
  • Presents results to the AI model
MCP Server (your custom code):
  • Exposes "tools" (functions the AI can call)
  • Exposes "resources" (data the AI can read)
  • Handles requests and returns results
Communication: Uses JSON-RPC over stdio (local) or HTTP/SSE (remote)

What MCP Servers Provide

Tools: Functions the AI can call
  • query_database(sql) — run a SQL query
  • create_jira_ticket(title, description) — create a ticket
  • send_slack_message(channel, text) — post to Slack
  • deploy_to_staging() — trigger a deployment
Resources: Data the AI can read
  • Database schemas
  • API documentation
  • Configuration files
  • Live system metrics
Prompts: Pre-built prompt templates
  • Reusable instruction sets
  • Domain-specific workflows
  • Best practice templates

Building Your First MCP Server

Step 1: Setup

Create a new Node.js project for your MCP server. Install the MCP SDK from npm — the package is called @modelcontextprotocol/sdk.

Initialize your server with a name and version.

Step 2: Define Tools

Each tool needs:

  • name: What the AI calls it (e.g., "query_database")
  • description: What it does (AI uses this to decide when to call it)
  • inputSchema: JSON Schema defining the parameters
  • handler: The function that runs when called
Example: Database Query Tool Define a tool named "query_database" with a description like "Execute a read-only SQL query against the production database." The input schema requires a "sql" parameter of type string.

The handler function: 1. Receives the SQL query from the AI 2. Connects to your database 3. Executes the query (with safety checks — read-only!) 4. Returns the results as formatted text Example: Slack Notification Tool Define a tool named "send_slack_message" that takes a channel and message text. The handler uses the Slack Web API to post the message and returns a confirmation.

Step 3: Define Resources

Resources expose data the AI can read without calling a function: Example: Database Schema Resource Expose your database schema so the AI knows your table structure when writing queries. The resource URI might be "db://schema" and it returns your CREATE TABLE statements. Example: API Documentation Resource Expose your internal API docs so the AI can reference them when building integrations.

Step 4: Connect to Your Editor

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Claude Code

Add MCP servers to your Claude Code configuration. In your project's .claude/settings.json or global settings, add:

mcpServers configuration with:

  • Server name (e.g., "my-database")
  • Command to start it (e.g., "node")
  • Args pointing to your server file
  • Environment variables (database URL, API keys)
Restart Claude Code and it auto-discovers the tools.

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Cursor

Cursor supports MCP through its settings. Add the MCP server configuration in Cursor's settings panel under the MCP section. Same format — command, args, environment variables.

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VS Code (with Copilot)

VS Code is adding MCP support in 2025. Configuration goes in .vscode/settings.json under the MCP section.

Practical MCP Server Examples

1. Shopify Store MCP Server

Give your AI access to your Shopify store: Tools:
  • get_orders(status, date_range) — fetch recent orders
  • get_product(id) — get product details
  • update_inventory(product_id, quantity) — adjust stock
  • get_analytics(metric, period) — revenue, traffic, conversion
Use case: Ask Claude "What were our top 5 products by revenue this week?" and it queries your Shopify store directly.

2. Project Management MCP Server

Connect to Jira, Linear, or Asana: Tools:
  • create_ticket(title, description, priority)
  • update_ticket(id, status)
  • get_sprint_status()
  • assign_ticket(id, user)
Use case: "Create a bug ticket for the checkout page crash on mobile" — Claude creates it directly in your project management tool.

3. Deployment MCP Server

Automate your CI/CD: Tools:
  • deploy(environment, branch)
  • get_deploy_status(deploy_id)
  • rollback(environment)
  • get_logs(service, lines)
Use case: "Deploy the main branch to staging and show me the last 50 log lines" — Claude handles the entire process.

4. Documentation MCP Server

Give AI access to your internal docs: Resources:
  • Architecture decision records
  • API specifications (OpenAPI)
  • Runbooks and playbooks
  • Team conventions and standards
Use case: "How does our authentication flow work?" — Claude reads your internal docs and explains accurately.

5. Monitoring MCP Server

Connect to your observability stack: Tools:
  • get_metrics(service, metric, timerange)
  • get_alerts(status)
  • get_error_logs(service, level, count)
  • create_incident(title, severity)
Use case: "Are there any critical alerts right now? Show me error logs from the payment service" — Claude checks your monitoring in real-time.

Security Best Practices

Authentication

  • Store API keys in environment variables, never in code
  • Use service accounts with minimal permissions
  • Rotate keys regularly
  • Use OAuth for user-specific access

Authorization

  • Implement read-only by default
  • Add explicit write permission checks
  • Log all tool invocations
  • Rate limit dangerous operations

Data Safety

  • Never expose production write access in development
  • Sanitize SQL queries to prevent injection
  • Validate all inputs in tool handlers
  • Don't log sensitive data (passwords, tokens)

Network Security

  • Use stdio transport for local-only servers
  • Use HTTPS for remote MCP servers
  • Whitelist allowed endpoints
  • Implement request signing

MCP Ecosystem in 2025

Official MCP Servers

Anthropic and the community maintain servers for:
  • Filesystem: Read/write files
  • GitHub: Issues, PRs, repos
  • Slack: Messages, channels
  • PostgreSQL: Database queries
  • Google Drive: Documents, sheets
  • Brave Search: Web search

Community MCP Servers

Growing ecosystem of community-built servers:
  • MongoDB, MySQL, Redis connectors
  • AWS, GCP, Azure management
  • Figma, Linear, Notion integrations
  • Custom CMS connectors

The MCP Hub

A registry of available MCP servers — like npm for AI tool integrations. Browse, install, and configure servers with minimal setup.

The Future of MCP

MCP is becoming the standard for AI-tool integration:

  • More editors adopting it: VS Code, JetBrains, Zed are all adding support
  • Remote MCP servers: Run servers in the cloud, connect from anywhere
  • MCP marketplaces: Install pre-built integrations with one click
  • Composable AI workflows: Chain multiple MCP servers for complex operations
  • Enterprise MCP: Managed MCP infrastructure for teams

Getting Started Checklist

1. Identify your use case: What data/tools should your AI have access to? 2. Start simple: Build one tool that solves one real problem 3. Test locally: Use stdio transport for development 4. Add security: Auth, rate limiting, input validation 5. Document: Describe what each tool does and when to use it 6. Share with team: Version control your MCP servers 7. Iterate: Add more tools based on team feedback Need help building custom MCP servers for your development workflow? Let's build your AI toolkit.