Custom MCP Server Development
I build production-grade MCP servers that connect Claude, Cursor, and other AI assistants to your internal APIs, databases, and SaaS platforms.
The problem
Your team adopted Claude, GPT, or another AI assistant. It's great for generating text and answering questions. But it can't access your internal systems: your CRM, your database, your monitoring tools, your custom APIs. The AI lives in a bubble, disconnected from the data that would make it actually useful.
Model Context Protocol (MCP) solves this. It's the open standard that lets AI assistants call external tools: read emails, query databases, trigger workflows, manage infrastructure. But building a production-grade MCP server requires understanding the protocol, handling authentication, managing multi-tenant access, and shipping something that won't break in production.
What I build
Custom MCP servers that give your AI assistants secure, typed access to your business systems.
- Internal API integration. Connect your AI tools to your REST/GraphQL APIs with proper authentication, rate limiting, and error handling
- Multi-account support. One MCP server that handles multiple accounts, organizations, or tenants with proper isolation
- SaaS platform connectors. MCP servers for platforms that don't have official ones yet: your CRM, project management tool, billing system, or monitoring stack
- Data pipeline access. Let AI assistants query your data warehouse, read dashboards, or trigger ETL jobs through natural language
Every server I build includes TypeScript type safety, Zod runtime validation, comprehensive error handling, and documentation.
How I work
- Scope. We define which systems to connect and what operations the AI should be able to perform
- Build. I develop the MCP server with full type safety, input validation, and authentication
- Test. Integration testing against your real systems in a staging environment
- Deploy. I ship the server configured for your team's AI tools (Claude Code, Claude Desktop, Cursor, or your custom setup)
- Documentation. Your team gets a README with setup instructions, available tools, and configuration options
Typical turnaround: 1-2 weeks for a standard integration. Complex multi-system servers may take longer.
What I've shipped
I've built and open-sourced MCP servers used by developers worldwide:
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Multi-Account Google MCP Server. 83+ tools across Gmail, Drive, Calendar, Sheets, Docs, Contacts, and Search Console with native multi-account switching. Zod enum validation eliminates LLM hallucination on account names. MIT-licensed and community-adopted.
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Multi-Sentry MCP Server. Manages isolated Sentry error monitoring across multiple portfolio companies from a single configuration. Security enforced at OS process level. Includes handoff package generation for portfolio exits.
Who this is for
- Dev teams adopting AI assistants who need them connected to internal systems
- Agencies and studios managing multiple client environments that need multi-tenant AI tooling
- Companies using SaaS platforms without official MCP servers
- Open-source maintainers who want to ship an MCP server for their project