MCP (Model Context Protocol) is a communication standard - a universal protocol that lets AI agents interact with any service, tool, or data source. Think of it like an API, but designed specifically for AI. If your product has an MCP server, any MCP-compatible client can connect to it and use it natively: your own custom application, an IDE like Cursor, a desktop AI assistant, or any agent you build. One server, any client.

I build those MCP servers - for your product, your internal tools, or your AI workflows.

MCP servers that expose your database as AI-readable resources

Tool implementations: search, create, update, and query operations any AI agent can invoke

Resource endpoints: structured context your AI reads before responding

Authentication and rate limiting for production deployments

Integration with existing Node.js or REST API backends

MCP servers for AI-native developer workflows and IDE integrations (Cursor, Windsurf, Claude Code)

Teams building AI agent workflows that need live access to your product's data

Founders who want any AI agent to interact with their product natively

Developers building AI-native products where the agent needs real context, not general knowledge

Engineering teams who want their internal tools accessible to any MCP-compatible AI client

You describe what data or operations your AI agent needs to access

I design the MCP interface: resources it exposes, tools it registers, auth strategy

I build and test against a real MCP client

Handover includes full documentation so your team can extend it

This is a niche, rapidly growing capability. Very few developers have hands-on MCP production experience. I have built and shipped real MCP servers - including an open-source one published on GitHub.