MCP server development that gives AI safe access to your systems
I build Model Context Protocol (MCP) servers that expose your internal operations, data and APIs to AI clients such as Claude, with schema validation at the boundary so a model can only call what you allow, with inputs you have defined. MCP server development is one of the skills assessed by my Claude Certified Developer – Foundations credential.
- Custom MCP server and agent suite
- Schema validation at the boundary
- Claude cert includes MCP servers
Why build an MCP server
Without MCP, every AI feature needs its own glue code to reach your systems. With an MCP server, you describe your tools and data once, as named operations with typed inputs, and any MCP-capable client, including Claude Code and your own agents, can discover and use them. AI access to internal systems becomes a single, reviewable piece of software instead of prompts and API keys scattered across projects.
What I have built
My custom MCP server is internal developer tooling: it exposes structured operations to LLM clients, and every request is validated against a schema at the boundary before anything executes, so malformed or out-of-scope calls fail cleanly instead of reaching a database or API. It sits in a wider suite that also includes AI agents with Hindi/Hinglish voice and outbound outreach, the kind of autonomous work where a strict boundary matters most.
How I design an MCP server
Good MCP servers are boring in the right way: predictable names, strict inputs and errors a model can recover from. I keep each tool small, describe it in plain language the model can reason about, and treat every call as untrusted input. Security and evals are part of the design from day one; both are skills covered by my Claude certification.
- Narrow tools with clear names and descriptions
- JSON Schema on every input, validated before execution
- Read-only by default, writes behind explicit tools
- Secrets and auth kept server-side
- Structured errors the model can act on
- A log of every call for review
Rates from $20/hour
Hourly for open-ended work, fixed price for defined projects. The first call is free and there's no obligation.
Frequently asked questions
What is MCP server development?
Building a server that implements the Model Context Protocol, an open standard for connecting AI applications to tools and data. The server lists the operations it offers and their input schemas, and an MCP client such as Claude can call them. The work covers tool design, validation, auth, hosting and testing with real prompts.
Which languages do you use for MCP servers?
Python and TypeScript. On Python, Pydantic 2 is a natural fit for schemas and validation, and I use it across my FastAPI backends; TypeScript suits teams already on Node.js. Either way, the server can wrap your existing REST APIs, PostgreSQL or MongoDB databases, Google Workspace APIs or CRM.
Is it safe to let an AI model call our internal systems?
It can be, if the server is designed for it. The model only sees the tools you expose, every input is validated before it runs, write and delete actions can require human approval, and credentials never reach the model. Application security is one of the skills my Claude certification covers, and I apply it here.
How much does an MCP server cost to build?
Rates start from $20/hour. A server with a defined set of tools over an existing API is a good fit for a fixed-price quote, which I give after a free intro call where we list the operations you want to expose and who should be allowed to use them.