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

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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)

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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

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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.

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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.

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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.

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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

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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.

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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.

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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.

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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.

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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

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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

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Authorization

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

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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)

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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

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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

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Community MCP Servers

Growing ecosystem of community-built servers:

• MongoDB, MySQL, Redis connectors
• AWS, GCP, Azure management
• Figma, Linear, Notion integrations
• Custom CMS connectors

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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.