How to Build an MCP Server: Complete Tutorial · Alternative Angle

Building Your First MCP Server

This guide walks through creating a production-ready MCP server from scratch. You'll build a server that provides code search capabilities, using both the TypeScript and Python SDKs to understand the implementation options.

Prerequisites

Before building an MCP server, ensure you understand basic TypeScript or Python programming, have Node.js 18+ or Python 3.10+ installed, and are familiar with how AI assistants like Claude work with tools.

Project Setup

TypeScript Setup

Initialize a new Node.js project and install the MCP SDK. The SDK provides the abstractions you need to define tools, handle requests, and manage connections.

Create a source file for your server implementation and configure TypeScript for your target environment. The MCP SDK works with both CommonJS and ESM modules.

Python Setup

For Python implementations, install the mcp package from PyPI. Set up a virtual environment for dependency management. The Python SDK provides equivalent functionality to the TypeScript version with Python idioms.

Defining Your First Tool

Tool Schema Design

Each tool in an MCP server has a name, description, and input schema. The input schema uses JSON Schema to define what parameters the tool accepts. Write descriptions as if explaining to an AI colleague—when to use the tool, what inputs it needs, and what output to expect.

For a code search tool, define parameters for the search query, the repository to search, and optional filters like file extensions or line count limits.

Implementation Logic

The tool implementation handles the actual work. For code search, this means querying a code index, filtering results, and formatting the response. Return structured data that AI models can easily incorporate into their responses.

Error Handling

Robust error handling distinguishes production MCP servers from prototypes. Distinguish between recoverable errors (temporary service unavailability) where retry might succeed, and permanent errors (invalid input) where retry won't help. Always return structured error responses rather than letting exceptions propagate.

Testing Your MCP Server

Unit Testing Tool Logic

Write unit tests for your tool implementations independent of the MCP protocol. Mock external dependencies like search indexes or file systems. Test both success and failure paths.

Integration Testing

Test the full MCP server by connecting it to Claude Desktop or Cursor. Verify that tool descriptions appear correctly, inputs are validated properly, and outputs are formatted for AI consumption.

Testing Edge Cases

AI models can generate unexpected inputs. Test with malformed queries, extremely long inputs, special characters, and boundary conditions. Verify that your server handles these gracefully without crashing or returning confusing errors.

Deployment Considerations

Configuration Management

MCP servers typically require some configuration—API keys, database credentials, service endpoints. Use environment variables for sensitive values. Document all configuration options clearly for users.

Scaling

Stateless MCP server design makes horizontal scaling straightforward. Run multiple instances behind a load balancer for high availability. Monitor connection counts and resource usage to plan capacity.

Security

Review the security implications of each tool you expose. Implement authentication on all endpoints. Validate and sanitize all inputs. Log access for auditing. Start with minimal permissions and expand as needed.