Building MCP Servers: A Developer's Guide

What Is MCP?

The Model Context Protocol, introduced by Anthropic, is an open standard that lets AI models connect to external data sources and tools. Instead of every AI application building custom integrations, MCP provides a consistent way to expose resources, prompts, and tools to any MCP-compatible client.

Core Components of an MCP Server

• Resources: Read-only data the AI can reference (documents, records, settings).
• Tools: Functions the AI can call to perform actions (send email, update CRM, create task).
• Prompts: Predefined templates the AI can use to interact with the server.
• Roots: Contextual anchors that help the AI understand where it is operating.

How to Build One

1. Define capabilities: List what your app can expose to an AI agent.
2. Use the MCP SDK: Anthropic provides Python and TypeScript SDKs to scaffold a server quickly.
3. Implement tools: Wrap your existing API endpoints as MCP tool handlers.
4. Expose schemas: Each tool needs a clear name, description, and input JSON schema.
5. Register the server: Add it to the client with a name and transport settings.

OAuth and Security

MCP servers should never receive plain credentials. Use OAuth 2.0 to issue short-lived tokens. Crawlii’s MCP platform manages this automatically: users authenticate once, and the server injects tokens on the backend so the AI never sees secrets.

Context Resolution

A good MCP server resolves ambiguous inputs automatically. For example, if an AI is asked to “update the Salesforce lead for Acme Corp,” the server should identify the workspace, look up the record, and execute the update without the user writing IDs manually.

Frequently Asked Questions