MCP (Model Context Protocol) servers are the bridge between AI models and your business tools. I build custom MCP servers that give AI assistants direct access to your APIs, databases, and workflows, turning them from generic chatbots into powerful business tools.
What You Get
Custom MCP server development that connects AI coding assistants and chatbots to your specific business context. Whether you need Claude to query your database, Copilot to understand your internal APIs, or a custom AI agent to automate complex workflows, MCP servers make it possible.
- Custom MCP server development, TypeScript/Node.js or Python MCP servers built to your exact specifications with tool definitions, resource providers, and prompt templates
- API integration, Connect AI models to your existing APIs, SaaS tools, databases, and internal services through a standardized protocol
- AI agent development, Build autonomous AI agents that can perform multi-step workflows using your MCP servers as their toolkit
- Security & auth, Proper authentication, rate limiting, and access control so your MCP servers are production-ready
- npm packaging, Publish your MCP servers as installable packages for easy distribution across your team
- Documentation & training, Clear docs and team training so your developers can maintain and extend the servers
What Is MCP?
Model Context Protocol (MCP) is an open standard created by Anthropic that lets AI models interact with external tools and data sources. Think of it as a USB port for AI, a universal way to connect any AI model to any service.
Before MCP, connecting an AI model to your tools meant building custom integrations for each model. MCP standardizes this: build one server, and every MCP-compatible client (Claude Desktop, VS Code Copilot, Cursor, Continue) can use it.
MCP Servers I’ve Built
- DataForSEO MCP, Gives AI assistants access to keyword research, SERP analysis, backlink data, and competitor intelligence through natural language
- MCP, AI-powered web scraping: ask Claude to scrape the pricing page of competitor X and get structured data back
- Analytics MCP, Connect Google Analytics to AI: query traffic data, generate reports, and identify trends through conversation
- Chrome DevTools MCP, AI-controlled browser automation: navigate, screenshot, interact with web pages through MCP tools
Use Cases
Internal Tools
Let your team query internal databases, CRMs, and project management tools through AI assistants instead of learning complex UIs.
Customer-Facing AI
Build AI-powered customer support, sales assistants, or onboarding guides that have access to your full product catalog and documentation through MCP.
Developer Productivity
Give Copilot and Cursor context about your codebase, deployment pipeline, monitoring systems, and internal APIs. Stop copy-pasting documentation into chat windows.
Which AI clients support MCP?
Claude Desktop, VS Code (GitHub Copilot), Cursor, Continue, Windsurf, and most modern AI coding assistants. The ecosystem is growing rapidly.
How is this different from function calling?
Function calling is model-specific. MCP is universal, build once, use with any compatible client. MCP also provides resource browsing, prompt templates, and sampling that function calling doesn’t support.
What languages do you build MCP servers in?
Primarily TypeScript (Node.js) and Python, using the official Anthropic MCP SDKs. TypeScript for performance-critical servers; Python for data-heavy or ML-integrated servers.