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An MCP server exposes the CRM as permissioned tools, so an AI assistant can work with leads and deals on request.
MCP servers
An MCP server that lets AI assistants work with your product
Abdur Rehman is an MCP server developer who builds Model Context Protocol servers for SaaS products and internal systems. An MCP server gives AI assistants such as Claude a safe, permissioned set of tools for your data and actions. He has built MCP servers for a production CRM and for a commerce platform.
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This is for you if
- Your customers ask whether your product works with Claude, ChatGPT or their own AI assistant.
- Your team wants to ask questions of your internal systems in plain English.
- You are building agents and want one clean, reusable way to expose your tools.
What you get
A tool set designed for AI
A small number of well-named tools with clear inputs and outputs. Fewer, better tools beat a mirror of your whole API.
Permissions and limits
Authentication, per-user access, rate limits and logging, so an assistant can only do what that user may do.
Tested with real assistants
Checked end to end with Claude and other MCP clients, with example prompts your users can start from.
Docs and handover
Setup instructions for your customers and notes for your team on adding new tools.
What is an MCP server?
An MCP server is a small service that exposes your product’s data and actions to AI assistants through the Model Context Protocol, an open standard. Instead of a custom integration for every assistant, you publish one set of tools that any MCP-compatible assistant can use.
The protocol is documented at modelcontextprotocol.io.
When does a product need an MCP server?
When your users already work inside an AI assistant and want it to reach your product, or when you are building agents and need a clean, reusable tool layer. If you have neither, you probably need a normal API first.
Is it safe to give an AI assistant access to our data?
It is when the server is designed for it. Each tool checks who is asking and what they may see, write actions can require confirmation, and every call is logged. The assistant never gets direct database access.
Where I’ve done this
How a project goes
Choose the tools
We pick the handful of questions and actions that matter most to your users.
Design for the model
Names, descriptions and outputs shaped so an assistant uses them correctly.
Build with permissions
Authentication, per-user access, limits and logging from the first version.
Test and document
Verified with real assistants, then documented for your customers and team.
Questions people ask
Straight answers. If yours is not here, ask me.
Ask me directlyI read and answer every message.Which AI assistants support MCP?
Claude supports it natively, and support across other assistants and developer tools keeps growing. Because MCP is an open standard, one server works with every compatible client.
Do we need an API first?
It helps, but it is not required. The MCP server can sit on top of your existing API or talk to your database through a safe service layer.
How does pricing work?
After a short call I send a fixed quote for an agreed scope, so you know the cost before work starts. Larger builds are split into milestones.
Do I own the code?
Yes. The code lives in your repository and runs on your accounts. You own the code, the accounts and the data, so you are never locked in to me.
Guides on this
Guide5 min read
How to build an MCP server for your product
A first-hand guide to building a custom MCP server: choosing tools, writing descriptions for the model, enforcing permissions, transports and testing.
Related services
AI agents
Agents that use your tools and data, take approved actions and hand off to people when it matters.
Custom CRMs
One system for leads, calls, texts, contracts and follow-ups, shaped around your process.
Have a process eating your team’s time?
Tell me about it. I’ll tell you straight whether AI is the right answer.
You’ll talk to me directly.On the call and in every message.
Or email hello@abdurrehman.dev