MCP Server Development
Naman Gundaniya builds custom MCP (Model Context Protocol) servers that connect AI assistants like Claude to your internal tools, databases and documents — safely. MCP itself is an open protocol; the engineering is in the tool design: what the assistant can see, what it can change, and how every action is validated and logged.
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Who this is for
- Companies that want Claude or another AI assistant working with their internal data
- Engineering teams adopting Claude Code who need it wired to their own systems
- SaaS products exposing an MCP server so AI assistants can operate them
- Teams gluing AI to internal tools with brittle one-off scripts
What you get
- A custom MCP server exposing your systems as typed, permissioned tools
- Read/write separation, with explicit approval on destructive actions
- Authentication and per-user access control
- Schema-validated inputs and outputs on every tool
- Deployment in your infrastructure, with every call logged
- Documentation your team can extend with new tools
One protocol instead of a pile of integrations
MCP is an open standard that lets AI assistants connect to external tools and data through one interface. Instead of a bespoke integration per assistant per system, you build one server that exposes your database, CRM, docs or internal APIs as tools — and any MCP-compatible client can use them, within the permissions you set. It was introduced by Anthropic and has been adopted across the ecosystem, so a server built today is not a bet on a single vendor.
Your data stays yours
An MCP server runs in your infrastructure — locally, on-prem or in your cloud. The assistant never gets raw database access or a data export; it gets narrow tools that return exactly what each tool was designed to return. That inversion matters: rather than pasting internal information into a chat window, the AI comes to your systems through an interface you control, with authentication in front of it and a log of every call behind it.
Tool design is the whole game
The protocol is the easy part. The engineering judgement is in the tools: a search_customers tool that returns five fields is safe; a run_sql tool is an incident waiting for a prompt injection. Every tool gets a typed schema, validation that rejects malformed input, an explicit read-or-write classification, and human approval on anything destructive. This is the same discipline as production LLM integration — structured outputs, guardrails, evals — applied at the boundary between the model and your systems.
Claude Code for engineering teams
The fastest-growing MCP use case is internal: wiring Claude Code to a team's own world — internal APIs, documentation, ticketing, deployment status — so the coding agent works with real context instead of guesses. The result is an assistant that can look up the actual schema, read the runbook and reference the open ticket, without anyone pasting internal material into a chat window. Set up once, the whole team inherits it.
How the engagement works
- 01
Discovery
Identify which systems an assistant should reach first — and what it must never touch. The 'never' list matters more than the wish list.
- 02
Design
Specify each tool: inputs, outputs, permissions, and whether it reads or writes. Risky actions get approval steps, not warnings.
- 03
Build
Implement and test the server against real assistant sessions — Claude, Claude Code or your chosen client — with every call logged from the first run.
- 04
Ship
Deploy in your infrastructure with access control and monitoring, plus 24-hour response times for 30 days after launch.
Technology
- Protocol
- The official MCP SDKs in TypeScript and Python, so servers stay compatible as the protocol evolves.
- Integration
- REST and GraphQL APIs, PostgreSQL, MongoDB and document stores — exposed as narrow, typed tools rather than raw query access.
- Safety
- Per-tool permissions, schema validation that rejects on mismatch, audit logging, and human approval on write actions.
- Clients
- Claude and Claude Code first-class; the same server works with any MCP-compatible assistant, including OpenAI's clients.
Proof
Frequently asked
What is an MCP server?
An MCP server is a small service that exposes your systems — databases, CRMs, internal APIs, document stores — to AI assistants through the Model Context Protocol, an open standard. Instead of building a separate integration for every assistant, you build one server, and any MCP-compatible client can use its tools within the permissions you set.
Is MCP only for Claude?
No. MCP was introduced by Anthropic but is an open protocol adopted across the ecosystem — OpenAI and other major vendors support it, and thousands of public servers already exist. A server built for Claude today works with other compatible assistants without a rewrite.
Is it safe to give an AI access to internal systems?
It is exactly as safe as the tools you expose. The assistant never gets raw database access; it gets narrow, typed tools with explicit permissions, schema-validated inputs and human approval on anything destructive — and every call is logged. Done properly, an MCP server is a tighter interface than most internal admin panels.
What should we connect first?
The system your team most often copies information out of by hand — usually documentation, the CRM or the ticketing system — and read-only. A read-only first server delivers visible value in days and builds trust in the setup; write access can follow once the audit logs show how the assistant actually behaves.
Can you set up Claude Code for our engineering team?
Yes. That means wiring Claude Code to your internal APIs, docs and workflows through MCP so the agent works with your real context, plus the permissioning that keeps it inside its lane. It is the fastest way for a team to get leverage from AI coding agents without pasting internal code into chat windows.
Start with a 30-minute call
Scope the work, agree a timeline, and find out whether this is the right fit — no obligation either way.
Email Naman