MCP Server Development

The Integration Problem

When you use Claude, ChatGPT, or similar AI assistants, they're working in a sandbox. They can reason, write, and analyze—but they can't reach out to your systems directly. Want Claude to query your database? Check your project files? Interact with your internal APIs? Out of the box, it can't.

The Model Context Protocol (MCP) solves this by providing a standardized way for AI assistants to call external tools. I develop MCP servers that expose your systems' capabilities as tools the AI can use, with proper authentication, validation, and error handling built in.

How I Build MCP Servers

Every MCP server I build follows the same core principles: security first, clear tool definitions, and robust error handling. I want the AI to have useful capabilities, but within carefully defined boundaries.

A typical MCP server project involves:

• Requirements analysis: Understanding exactly what capabilities you need exposed and what constraints should be in place
• Tool design: Defining clear, well-documented tools that the AI can understand and use effectively
• Security implementation: Building in authentication, authorization, input validation, and audit logging
• Testing: Verifying the server works correctly across a range of scenarios, including edge cases
• Documentation: Providing clear setup instructions and tool references for your team

Common Use Cases

I've built MCP servers for a variety of purposes. Some patterns I see frequently:

Database Integration

Give Claude read access to your databases so it can answer questions about your data directly. I implement query builders that let the AI construct queries safely, with row limits and query validation to prevent runaway operations.

File System Access

Let Claude read, search, and understand your project files. I build file system MCP servers with configurable scope—maybe Claude can access your docs folder but not your secrets directory.

API Integration

Connect Claude to your internal APIs. CRM systems, project management tools, analytics platforms—any system with an API can become a tool Claude can use to answer questions and take actions.

Custom Business Tools

Sometimes the need is unique. I've built MCP servers for real estate analytics, inventory management, and specialized content processing. If your workflow needs AI integration, I can build the bridge.

Architecture & Security

MCP servers sit between your AI assistant and your infrastructure. I treat them as a security boundary, implementing:

• Input validation on every tool call
• Output sanitization to prevent data leakage
• Comprehensive logging for audit trails
• Rate limiting to prevent abuse
• Graceful error handling that gives the AI useful feedback without exposing system internals

I typically build MCP servers in Python, leveraging the official MCP SDK. They can run locally next to a single user's AI client, or be deployed on your infrastructure over streamable HTTP with bearer-token authentication, so Claude Code, desktop and web AI apps, and your own agents can all share them. When an application uses many servers, I add a planning step that picks the tools each request needs instead of loading every schema into every prompt.

Getting Started

If you're already using an MCP-capable AI client and want it to reach your own systems, an MCP server is likely the right approach. I start every project with a discovery conversation to understand:

• What systems and data do you want Claude to access?
• What actions should Claude be able to take?
• What are the security and compliance requirements?
• Who will be using the integration and how?

From there, I scope out the project, build the server, and work with your team to deploy and test it. The result is an AI assistant that actually knows your business.