MCP Server Development
MCP server development: plug AI agents into your systems, safely
An AI assistant is only as useful as what it can reach. We build Model Context Protocol (MCP) servers that let Claude and other AI agents read your data and use your tools, with permissions you control, limits you set and a log of every single call.
Sound familiar?
Problems we fix
If your AI assistant can’t reach the systems your team uses, these will sound familiar.
AI can’t see your systems
Your team has powerful AI assistants that know nothing about your customers, orders or tickets.
Copy-paste into chat
People paste customer data into chat tools by hand, and nobody can say what has left the building.
Security says no
Before an agent touches production data, you need clear permissions, limits and an audit trail. Rightly so.
Customers want AI integrations
Your users already work inside AI assistants and are asking when your product will show up there.
What we build
What you get
Data MCP servers
Read-only or read-write access to PostgreSQL, Supabase and MySQL, scoped to exactly the tables an agent needs.
Product MCP servers
Put your SaaS inside your customers’ AI assistants, so they can use your product without opening it.
Workflow tools
Let an AI chat start your n8n or Make.com workflows: “create the invoice”, “onboard this client”.
Auth & permissions
OAuth sign-in, scoped keys and per-tool permissions, so each user’s agent only sees what that user can.
Audit logs & rate limits
Every tool call recorded and rate-limited, ready for your security review.
Deployment & docs
Hosted in your own cloud, with documentation your team and your customers can follow.
How we build MCP servers
Useful to agents, safe for your data
A good MCP server is designed for how an AI reads and chooses tools, and locked down for how a security team thinks. We build for both.
- 1
Decide what to expose
Together we choose which data and actions agents should have, and which they never should.
- 2
Design the tools
Clear names, descriptions and inputs, so an AI picks the right tool for the job. This is where most MCP servers go wrong.
- 3
Lock down access
Sign-in, scoped keys and per-tool permissions, so each user’s agent can only do what that user is allowed to.
- 4
Build and test
Automated tests for every tool, then real sessions in AI clients to check agents use it the way you intended.
- 5
Add limits and logging
Rate limits, input checks and a log of every call, ready for your security review.
- 6
Deploy and document
Hosted where you need it, with documentation for your team and, for product servers, for your customers.
Cost & timeline
What affects the price
MCP servers are scoped after a free call. These are the things that shape the price.
- How many tools
- Five well-chosen tools over one database is a small build. Fifty tools across several systems is a larger one.
- Read-only or read-write
- Letting agents change data needs stricter permissions, validation and approval steps than read-only access.
- Who uses it
- An internal server for your own team is simpler than a product server your customers connect to.
- Security requirements
- OAuth, single sign-on and audit requirements add work, and are worth it for production data.
- Hosting and support
- Where it runs, and whether we look after it once it’s live.
Industries
Where MCP servers help, by industry
MCP Server Development FAQs
What is MCP?
The Model Context Protocol is an open standard for connecting AI applications to external tools and data through a common interface.
Which AI clients can use our MCP server?
Claude supports MCP natively, and so do a growing list of other AI assistants, IDEs and agent frameworks. We’ll test your server in the clients your team and customers actually use.
Is it secure?
We use least-privilege access, authentication, input validation and audit logs on every tool call.
Can our customers use our MCP server?
Yes. A product MCP server lets your customers work with your product from their own AI assistant.
Do we need MCP, or just an API?
If your goal is for AI assistants and agents to use your systems, MCP is the standard way to do it, and it works across clients. If only your own code calls the service, a normal API may be enough.
Can you build MCP on top of our existing API?
Yes, that’s the most common starting point. We wrap your API in clear, well-described tools with the right permissions, without changing the API itself.
How do you test an MCP server?
Automated tests for every tool, plus real sessions in AI clients such as Claude to check that agents pick the right tool and handle errors well.
Where does it run?
In your own cloud, close to your data, or as a hosted service for your customers. We set up deployment, monitoring and logging either way.
Often paired with
Related services
AI Agents & LLM Apps
Assistants that answer from your documents, use your tools and hand over to a person when they should, tested before they meet a customer.
Learn more →AI Full-Stack Development
AI products that hold up with real users: SaaS platforms, copilots and AI features inside your app, with the unglamorous parts (auth, billing, testing and monitoring) done properly.
Learn more →Internal Tools & Admin Panels
Replace the spreadsheet everyone fights over with a dashboard or back office your team actually likes using, built to a fixed-scope estimate.
Learn more →Let’s connect your AI to the systems it needs.
Tell us which databases, APIs or tools your AI should use. On a free 30-minute call we’ll map the tools, the permissions and what to build first.
Official Retool Agency Partner5.0 on Clutch, 8 verified reviewsBuilding software since 2015Reply within one business day