AI and data · MCP servers
MCP server development that connects AI assistants to your systems safely
The Model Context Protocol lets Claude, ChatGPT, Cursor and your own agents read your data and take actions through one standard interface. We design, build and host MCP servers with the authentication, permissions and audit trail your security team will ask about.
Short answer
An MCP server is a small service that exposes your APIs, databases or product features as tools and resources any MCP-compatible AI client can use. Innovation Insight builds production MCP servers with OAuth, per-user permissions, rate limits and logging, from $8k for a single-system server over three to five weeks to $40k or more for a multi-tenant server shipped as part of your SaaS product.
Reviewed by Zain Khalid Malik, CTO & Co-founder · Updated
What an MCP server is, in plain terms
The Model Context Protocol (MCP) is an open standard, introduced by Anthropic and now supported by OpenAI, Google, Microsoft and most AI developer tools, that defines how an AI application talks to outside systems. Before MCP every integration was custom: one plugin format for ChatGPT, another for your internal agent, another for your IDE. With MCP you build one server that describes what your system can do, and every compatible client can use it. The server exposes three kinds of things: tools (actions such as "create invoice" or "search tickets"), resources (read-only data such as a customer record or a document) and prompts (reusable instructions for common tasks).
Who needs one
- SaaS companies whose customers want to use the product from inside Claude, ChatGPT or Copilot. An official MCP server is quickly becoming as expected as a public API.
- Internal teams that want an AI assistant to query the data warehouse, CRM, ticketing and wiki without copying data into another tool.
- Engineering teams building their own agents who want a clean, testable boundary between the model and the systems it touches.
- Platforms that already have an API and want AI clients to use it correctly instead of guessing at endpoints.
What we build
| Component | What it does | Typical choices |
|---|---|---|
| Tool design | Turns your API into a small set of well-named, well-described tools a model can choose correctly | Task-shaped tools rather than one tool per endpoint; typed input schemas |
| Transport | How clients connect | Streamable HTTP for hosted servers, stdio for local and desktop use |
| Authentication | Proves who the user is and what they may do | OAuth 2.1 with your identity provider, scoped tokens, per-tenant keys |
| Authorisation | Limits each tool to the data the user can already see | Your existing permission model enforced server-side, never in the prompt |
| Safety controls | Stops a confused or manipulated model from doing damage | Confirmation for destructive actions, rate limits, allow-lists, input validation |
| Observability | Shows who called what, with which arguments and result | Structured logs, traces and an audit table your compliance team can query |
| Hosting | Where it runs | Your cloud (AWS, Azure, GCP), Cloudflare Workers or Vercel; containers or serverless |
The part that decides whether an MCP server is useful is tool design. Exposing 200 raw API endpoints overwhelms a model and produces wrong calls. We start from the ten to twenty jobs your users actually want the assistant to do, design tools around those jobs, write descriptions the model can reason about, and test them against real prompts in Claude, ChatGPT and your own agent before release.
Security is the product
An MCP server gives an AI model the ability to act on your systems, so the security design comes first. Every tool call is authorised against the signed-in user, not a shared service account. Destructive or financial actions require explicit confirmation. Inputs are validated as strictly as any public API, because prompt injection means the arguments can come from text the model read in an email or a web page. Secrets never pass through the model, and every call is logged with the user, the tool, the arguments and the outcome.
What it costs
| Scope | What is included | Range | Timeline |
|---|---|---|---|
| Internal server over one system | 8 to 15 tools, OAuth with your identity provider, logging, deployment in your cloud | $8k to $15k | 3 to 5 weeks |
| Multi-system internal server | Several back ends (CRM, warehouse, ticketing), per-user permissions, audit dashboard | $15k to $28k | 5 to 8 weeks |
| Public MCP server for your SaaS | Multi-tenant OAuth, usage metering, docs, listing in client directories, load testing | $25k to $40k+ | 6 to 10 weeks |
Hosting is usually small: most MCP servers run on serverless or a single container for tens of dollars a month. A support retainer from $1,500 a month covers protocol updates, new client versions and new tools as your product grows.
How a project runs
- Discovery (1 week): list the jobs users want done, map them to systems, agree the permission model and which actions need confirmation.
- Tool design and prototype (1 to 2 weeks): a working server with the core tools, tested live in Claude and ChatGPT with real prompts.
- Hardening (1 to 3 weeks): OAuth, rate limits, validation, logging, error messages the model can recover from, automated tests for each tool.
- Release: deployment, documentation, client configuration guides and, for public servers, directory submissions.
- Iteration: usage review each month to find tools the model misuses or jobs users ask for that are not covered yet.
Related work
Our AI team has built retrieval and agent systems for clients including an AI contract analysis platform for government contractors (a RAG pipeline on AWS Bedrock that extracts over 50 data points per contract) and a multi-tenant AI document processing SaaS on AWS Lambda. MCP servers often sit alongside those systems: the RAG pipeline answers questions, and the MCP server lets an assistant act on the answers. See our AI agent development and RAG development pages for those pieces.
Sources and further reading
Next step
Tell us what you're building and get a written estimate.
A senior engineer replies within one business day. NDA on request.
Products we've shipped, and what happened next.
Case studies written from the technical documentation of each project: the stack, the scale and the outcome.
Questions we get asked a lot.
Which AI clients can use an MCP server?
Claude (desktop, web and Claude Code), ChatGPT, Microsoft Copilot Studio, Cursor, VS Code, Windsurf and most agent frameworks, including the OpenAI Agents SDK and LangChain. One server works with all of them.
Is MCP secure enough for production?
The protocol supports OAuth and scoped access, but security depends on the server you build. We enforce your permission model on every call, require confirmation for risky actions and log everything, so the server is no less safe than your existing API.
Do we need an MCP server if we already have a REST API?
Your API is the foundation, and the MCP server is a thin layer over it. Without one, each AI client needs custom glue code and models often call endpoints incorrectly. With one, every compatible client works the same way.
Can you build it in our language and cloud?
Yes. We build MCP servers in TypeScript, Python, Go or C# and deploy them to AWS, Azure, GCP, Cloudflare or Vercel, inside your account.
How long does it take?
A focused internal server is usually in use within three weeks. A public, multi-tenant server for a SaaS product takes six to ten weeks including testing and documentation.
Can you review an MCP server we already built?
Yes. A security and tool-design review takes about a week and covers authentication, authorisation, injection risk, logging and how reliably models choose the right tools.