Give Your AI Assistant Access to Your Data
Right now, using AI with your business data means copying information from one system and pasting it into a chat window. MCP (Model Context Protocol) eliminates that friction. A custom MCP server connects your databases, APIs, and internal tools directly to AI assistants like Claude. The AI queries your systems through structured tool interfaces instead of relying on whatever you paste into the prompt.
I build MCP servers with TypeScript using the official MCP SDK. Every server ships with typed tool definitions, input validation, access controls, and error handling. Your data stays behind the same security boundaries you already enforce. The AI gets structured access to exactly what it needs, nothing more.
How MCP Works
MCP is an open protocol that defines how AI models interact with external tools. I wrote a detailed breakdown of how MCP servers connect AI to business data. An MCP server exposes a set of tools, each with a name, description, and typed parameters. When you ask Claude to "look up the latest order from Acme Corp," Claude calls your MCP server's search_orders tool with the customer name as a parameter. The server queries your database, formats the result, and returns it to Claude. No manual data copying. No screen switching.
Amazon Creators API is an MCP server I built that wraps the Amazon Product Advertising API. It exposes product search, item lookup, and variation queries as MCP tools. Any MCP-compatible AI assistant can search Amazon's catalog, retrieve pricing, and generate affiliate links through natural language requests. The server handles authentication, request signing, response parsing, and error recovery behind the tool interface.
What You Can Connect
Any system with an API or database can become an MCP data source. CRM systems so your sales team can ask Claude about customer history. Project management tools so developers can query sprint data without leaving their editor. Inventory databases so operations teams can check stock levels through natural language. Internal knowledge bases so every employee gets instant access to company documentation.
I also build MCP servers that wrap existing REST APIs into AI-friendly tool interfaces. If you already have an API, I map the endpoints to MCP tool definitions, handle authentication, and transform responses for optimal LLM consumption. The AI gets clean, structured data instead of raw API responses.
Security and Access Control
MCP servers do not give AI unrestricted database access. Each tool is a scoped operation with defined inputs, outputs, and permissions. A read-only tool for customer lookup cannot modify records. A reporting tool can aggregate data without exposing individual entries. I implement tool-level access controls, input validation with Zod, audit logging of every tool call, and rate limiting per user and per organization.
For sensitive data, I build MCP servers that filter results based on the requesting user's permissions. A support agent sees customer-facing data. A manager sees financial summaries. An admin sees everything. The same server serves all three roles with different tool configurations.
The Protocol Is the Moat
MCP is supported by Claude Desktop, Claude Code, Cursor, Windsurf, and a growing list of AI development tools. I use these tools daily through Claude Code workflows that integrate directly with MCP servers. Building on MCP today means your AI infrastructure works with every future tool that adopts the standard. I build servers that work across all MCP-compatible clients without modification. As AI assistants become the primary interface for knowledge work, the companies with structured data access through MCP will have a significant advantage over those still copying and pasting.
Most MCP work ships as part of an AI Development or AI Automation engagement rather than standalone. The MCP server is the data layer; the AI application or workflow is what your team actually uses. For marketing and SEO support, I partner with Frog Stone Media.
