MCP servers that let AI agents use your systems, under your rules.
The Model Context Protocol is how AI assistants such as ChatGPT, Claude, Microsoft Copilot and Gemini connect to outside systems. Our MCP server development work is for software companies that want their product usable from those assistants, and for businesses that want their own agents to reach internal data safely.
On this page
What an MCP server does
An MCP server sits in front of a system, such as your product's API, a database or an internal tool, and describes what an AI agent may do with it: the tools it can call, the data it can read, and the prompts it can use. Any assistant that supports the protocol can then connect to it, without a separate integration for each AI vendor.
Anthropic introduced the protocol in November 2024. In December 2025 it was contributed to the Agentic AI Foundation, a Linux Foundation fund co-founded by Anthropic, Block and OpenAI, with Google, Microsoft and AWS among its members. The foundation's announcement counted more than 10,000 published MCP servers, and support in Claude, ChatGPT, Microsoft Copilot, Gemini, Cursor and VS Code. The specification is open, and the current version is dated 28 July 2026.
Who needs one
- Software companies whose customers are asking to use the product from ChatGPT, Claude or Copilot.
- Businesses building their own agents, which need controlled access to a CRM, an ERP, a document store or a database.
- Teams already using AI assistants internally who want them to read company data without copying it into a chat window.
What we build
- Remote MCP servers over HTTP, with OAuth sign-in, so each user connects with their own account and sees only what they are allowed to see.
- Local servers for desktop tools, where credentials come from the user's own machine.
- Tools designed for an AI caller: a small set of well-named actions with clear inputs, instead of every endpoint in your API exposed one-to-one.
- Read-only access by default, and write actions behind explicit scopes, confirmation steps and limits.
- Audit logs of every call: who, which tool, what arguments, what came back.
- Tests that run the server against real assistants, because a tool description that reads well to a developer can still confuse a model.
Security is most of the work
The protocol's own specification is direct about the risk: tools are arbitrary code execution, users must consent before a tool runs, and a tool's description should be treated as untrusted unless it comes from a trusted server. An MCP server gives an AI agent a way into your systems, and an agent can be steered by what it reads.
So we design for it:
- Authorization follows the MCP specification, which is built on OAuth 2.1. Tokens are issued for your server alone, and the server rejects any token issued for something else.
- Scopes are narrow. An agent asking for more access has to ask the user again.
- Text an agent reads from your data (a customer note, an email, a ticket) is treated as data. The server never lets it change which tools run or what they are allowed to do.
- Rate limits, and limits on how much data a single call can return.
- Anything that moves money, deletes records or contacts customers needs a person to confirm it.
Our ISO/IEC 27001:2022 certificate covers AI-enabled applications and the integration of generative AI models by name. Read the scope.
How a project runs
- We start with the use cases: what an agent should be able to do for your users, and what it must never do.
- We design the tool set and the permission model, and agree it with you in writing.
- We build in two-week increments, testing against the assistants your customers use.
- It launches with monitoring, a runbook, and a named engineer on call.
What it costs
A read-only server over one well-documented API is a small project. A remote server with OAuth, per-user permissions, write actions and audit logging, across several systems, is a larger one. We fix the price once the tool set and permission model are agreed. Our guide to what AI agent development costs covers the running costs on the agent side.
The Automation Audit
One workflow you name, mapped end to end, with the arithmetic done before anyone writes code.
- Scope
- One workflow you choose, traced end to end, including the steps nobody documented.
- Duration
- Two weeks, fixed.
- Fee
- Fixed, and quoted in full before we start. No hourly drift.
- You get
- A written map: what can be automated, what it would save in hours, what building it would cost, and what we would leave alone.
- You keep it
- The map is yours whether or not you hire us to build anything.
And if the audit shows the automation will not pay for itself inside twelve months, we will tell you, and we will not quote the build.
Questions we get asked about MCP servers
Do we need an MCP server if we already have an API?
Your API is what the MCP server calls. The server adds what an AI agent needs on top: tools shaped around tasks, descriptions a model can follow, sign-in that works from inside an assistant, and limits suited to a caller that can make mistakes.
Which AI assistants will it work with?
Any assistant that supports MCP. Each one adds its own review or listing process for public servers, and some features arrive in one assistant before another, so we test against the specific ones you care about.
Can an agent do damage through it?
Only what the server allows. So we start from read-only, keep write scopes narrow, and put a person in front of anything irreversible.
Can you build an agent that uses our MCP server too?
Yes. The same team builds agents on the Applied AI side, and our custom API integrations page covers the systems behind it.
