The Model Context Protocol (MCP) is Anthropic’s open standard for connecting AI models to external data sources and tools in a consistent, reusable way. Instead of every AI application implementing its own custom integrations from scratch, MCP provides a universal protocol that works everywhere. Think of it like USB for AI: one standardized protocol enables endless connections without reinventing the wheel every time.
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The Problem MCP Solves
Before MCP existed, every AI application needed to implement completely custom integrations for each data source or tool it wanted to connect to. There was no standardization whatsoever, which meant massive duplication of effort across the entire industry.
With MCP, you have one universal protocol that handles all integrations in a consistent way. Write your integration once as an MCP server, and it works everywhere across any MCP-compatible client without modification.
What is MCP?
MCP is a protocol that defines exactly how AI models should communicate with external resources, similar to how HTTP defines web communication standards. It provides a clear specification that both AI applications and data providers can implement to enable seamless integration.
The three main components: The MCP Client lives inside your AI application and initiates requests, the MCP Server provides access to your data sources and tools, and the MCP Protocol itself defines the communication standard that both sides follow.
What MCP provides access to: Resources are the data that AI can read like files or database records, Tools are actions that AI can perform like creating issues or sending messages, and Prompts are reusable templates that guide the AI’s behavior for specific tasks.
How MCP Works
The architecture flows like this: Your AI application contains an MCP Client that communicates through the MCP Protocol to one or more MCP Servers that provide access to databases, files, APIs, and other external resources.
The typical interaction flow works like this: The AI asks the server what capabilities it has available, the server responds with its list of resources and tools, the AI decides what it needs and requests specific data or tool execution, the server returns the results, and finally the AI incorporates those results into its response to the user.
Building an MCP Server
Here’s a basic example of building your own MCP server using the official SDK to provide customer data access to AI applications:
import { Server } from "@modelcontextprotocol/sdk/server/index.js";
const server = new Server({ name: "example-server", version: "1.0.0" });
// Define resources (data AI reads)
server.setRequestHandler("resources/list", async () => ({
resources: [{ uri: "file:///data/customers.json", name: "Customer Data" }]
}));
// Define tools (actions AI performs)
server.setRequestHandler("tools/list", async () => ({
tools: [{ name: "get_customer", description: "Get customer by ID" }]
}));
server.setRequestHandler("tools/call", async (request) => {
const customer = await getCustomerById(request.params.arguments.customerId);
return { content: [{ type: "text", text: JSON.stringify(customer) }] };
});Real-World Examples
Database integration: User asks “Show me all orders from last week” and the AI calls your query_database() tool with the appropriate SQL, retrieves the results, and formats them into a readable summary for the user.
File system access: User asks “Find all TODO comments in my codebase” and the AI calls your search_files() tool to scan the project, collects all the matches, and presents them as an organized list.
Multi-system orchestration: User says “Create a GitHub issue for this bug and notify the team in Slack” so the AI calls github_create_issue() to file the bug, then calls slack_send_message() to notify your team channel, and confirms both actions completed successfully.
Using MCP in Claude Desktop
To connect MCP servers to Claude Desktop, you configure them in your Claude Desktop config file located at ~/Library/Application Support/Claude/claude_desktop_config.json:
{
"mcpServers": {
"filesystem": {
"command": "npx",
"args": ["-y", "@modelcontextprotocol/server-filesystem", "/path"]
},
"database": {
"command": "python",
"args": ["-m", "mcp_server_postgres", "--connection-string", "postgresql://localhost/mydb"]
}
}
}Once you’ve configured your MCP servers, Claude automatically has access to all their capabilities. You can simply ask Claude something like “List all files in my projects directory” and Claude will use the appropriate MCP server to fetch and display the results.
Security Considerations
Authentication is critical: Always verify authentication tokens before processing any requests from MCP clients to ensure only authorized applications can access your data and tools.
Implement proper permissions: Use role-based access control to limit what different users can do, rigorously validate all inputs before executing any operations, and implement SQL injection prevention by using parameterized queries instead of string concatenation.
Validate and restrict dangerous operations: Block or carefully control destructive database operations like DROP, TRUNCATE, and DELETE commands to prevent accidental or malicious data loss.
MCP Ecosystem
Official MCP servers from Anthropic: The core team maintains servers for common use cases including filesystem access, GitHub integration, PostgreSQL databases, and SQLite databases that you can use immediately.
Community-built servers: The open-source community has created servers for AWS services, Google Workspace integration, Slack messaging, Jira project management, MongoDB databases, Redis caching, and countless custom internal systems.
Discovering available servers: You can find MCP servers by searching npm with npm search mcp-server or browsing GitHub repositories tagged with the topic:mcp label to see what’s available.
Best Practices
Write clear, detailed descriptions: Thoroughly document what each tool does, what parameters it accepts, what format it expects those parameters in, and what it returns so the AI can use your tools effectively.
Provide comprehensive schemas: Include proper type definitions, enumerate valid options when there’s a fixed set of choices, add descriptions for every field, and clearly mark which fields are required versus optional.
Return structured, consistent responses: Always use consistent JSON formatting with clear status indicators, the actual data payload, error messages when things go wrong, and timestamps for when operations occurred.
Implement rate limiting: Set reasonable per-user or per-application rate limits to prevent abuse, accidental or intentional, from overwhelming your backend systems.
Conclusion
MCP is revolutionizing how we integrate AI with external systems by providing a universal standard that’s simple to implement, works consistently across all applications, and is completely open source for anyone to use and extend.
Getting started is straightforward: Install an existing MCP server that fits your needs, configure it in Claude Desktop’s settings file, start using it immediately through natural language requests, and when you’re ready, build custom servers for your own proprietary systems.
Learn more at the official documentation: https://modelcontextprotocol.io
MCP is USB for AI. One standard enables infinite possibilities.