MCP server build
We build the server that exposes your systems as MCP tools — each one named and typed like a contract, with inputs, outputs and errors defined so any AI client can use it without guessing.
AI · MCP
MCP server development — expose your systems to AI, cleanly
We build MCP servers — the Model Context Protocol layer that lets Claude and other AI clients use your systems through tools defined like a contract, scoped tight and logged. Build it once, and every MCP-aware agent can reach your data without a bespoke integration each time.
MCP
the tool standard AI is adopting
Scoped
permissions per tool, real auth
Reusable
across every MCP client
A one-off AI integration works once. An MCP server works for every agent you build next.
The value is not the plumbing between one model and one system — it is that MCP is a standard, so the tools you define are reusable across every client and agent that speaks the protocol. Get the boundary right — real auth, permissions scoped per tool, actions logged — and you have exposed your systems to AI without handing the model the keys. That boundary is the part we obsess over.
What we build
The parts that decide whether it works.
Auth & permission scoping
Your real auth sits in front — OAuth, API keys or your existing sessions — and every tool is scoped to only the data and actions it needs. The model holds a token that can do what you allowed and nothing else.
Read tools & safe actions
Read-only tools ship freely; tools that change something get confirmation on irreversible steps and full logging, so actions are safe because the boundary is built properly — not hoped about.
Self-host or managed
Runs on your infrastructure when your data cannot leave your boundary, or hosted and maintained by us when you would rather not babysit a service. You own the code either way.
How we work
Scoped, shipped weekly, handed over clean.
01
Map the tools
We define which systems to expose, which tools the AI needs, and where auth and confirmation belong — with a fixed estimate before any code.
02
Build & test
We ship working tools in a live environment weekly, tested against a real MCP client so you see the AI actually using them, not a mock.
03
Harden & hand over
Permission scoping, logging, monitoring and readable code your team can own — self-hosted or managed, with support after launch.
Where it fits
What teams hire us to build.
- Expose your product to Claude and other AI clients as tools
- An internal MCP server over your database and APIs
- A connector so agents can act in your CRM or ticketing system
- Read-only tools for safe AI querying of sensitive data
- A multi-source server wiring several systems into one tool set
- A managed MCP layer we host and maintain for you
Compared
MCP server vs a traditional API integration vs custom function-calling
| Duskel MCP server | Traditional API integration | Custom function-calling | |
|---|---|---|---|
| How the AI decides what to do | The AI discovers your tools and their contracts at runtime and chooses which to call | It does not — a human wrote fixed code that calls the API on a fixed path | You hardcode each function and hand-describe it to the model per app |
| Reusability across agents & clients | Build once, every MCP-aware client and agent can use it | Tied to the one app the integration was written into | Locked inside one codebase; the next agent needs it rewritten |
| Setup effort | Moderate up front — the server and tool contracts — then near-zero to reuse | Low for one path, but you pay it again for every new consumer | Low to start, high in total once you have several agents |
| Maintenance | One server to update when a system or the spec changes | Every integration updated separately when the API shifts | Scattered tool glue in each app, updated one by one |
| Best for | Exposing systems to AI you will reuse across agents and clients | A single fixed, non-AI system-to-system data flow | A quick one-model, one-app prototype you will not reuse |
What it costs
What an MCP server costs
Single-source connector
from $2k
2–3 weeks
One system, a focused set of tools, real auth and logging. The clean way to expose a single product, database or API to Claude and other MCP clients.
Multi-source / multi-tool
from $6k
4–8 weeks
Several systems wired into one tool set, read tools and scoped actions, confirmation on the risky ones. For when the AI needs to reach across your stack, not just one corner of it.
Enterprise / managed
let's talk
ongoing
We host, monitor and maintain the server, keep it current as your systems and the MCP spec move, and stay on a retainer. For teams who want the layer running without owning the upkeep.
These are typical ranges, not a menu — every system has its own auth and edge cases. You get a fixed quote before a line of code.
Questions
What clients ask before we start.
What is an MCP server, in plain terms?
MCP — the Model Context Protocol — is a standard way to hand an AI model a set of tools it can call: read this record, run this query, file this ticket. An MCP server is the small service that exposes your system as those tools, with the inputs, outputs and permissions defined like a contract. Build it once, and any MCP-aware AI client can use your system without a bespoke integration each time.
How is an MCP server different from plain function calling?
Function calling is the mechanism — the model emits "call this function with these arguments." MCP is the standard around it: a defined protocol for discovering tools, describing them, and calling them across a boundary. Hand-rolled function calling lives inside one app and one codebase; an MCP server is reusable across every agent and client that speaks the protocol, so you are not rewriting the same tool glue for each new project.
Which AI clients and models can use an MCP server?
Any client that speaks MCP — Claude and its desktop and code tools, a growing list of IDEs and agent frameworks, and your own apps once you wire the client side in. The model behind it is your choice: Claude, GPT, or an open model you self-host. The whole point of building on the protocol is that you are not married to one client or one vendor.
How do you handle authentication and access control?
The server sits between the AI and your systems, so that is exactly where auth belongs. We put your real auth in front — OAuth, API keys or your existing session — and scope each tool to only the data and actions it needs. A read tool cannot write; a tool for one customer cannot see another. The model never holds your raw credentials, it holds a token that can only do what you allowed.
Can it only read, or can it take actions too — and is that safe?
Both, and the split is deliberate. Read-only tools are low risk and we ship those freely. For tools that change something — refund a charge, delete a record, send a message — we scope tight, require confirmation on irreversible actions, and log every call so you can see exactly what the AI did and why. Actions are safe when the boundary is built properly, not when you cross your fingers.
Should we self-host the MCP server or have you manage it?
Depends on your constraints, and we will tell you straight. If your data cannot leave your boundary, we build it to run on your own infrastructure and hand it over. If you would rather not babysit a service, we host and maintain it for you. Either way you own the code — managed hosting is a convenience, not a lock-in.
What does an MCP server cost to maintain?
Less than you would guess, because a well-scoped server is a small, stable thing — the tools change only when your systems or the protocol do. The running cost is a modest server bill; the maintenance is the occasional update when an underlying API shifts or the MCP spec moves. We lay both out before you commit, and offer a retainer if you want us watching it.
How long does it take to build an MCP server?
A single-source connector — one system, a handful of tools — is typically two to three weeks. A multi-source server wiring several systems and actions together is more like four to eight, depending on how gnarly the auth and the edge cases are. You get working tools in a live environment each week, not a status deck at the end.
Can it connect to our existing systems?
That is the whole job. If your system has an API, we build tools against it; if it only has a database, we go in through a safe query layer; if it is an old internal thing with neither, we usually still find a way in. The server is the adapter between the AI and whatever you already run — we do not need you to rebuild anything to make it work.
Why build an MCP server now rather than wait?
Because the clients and the buyers are standardising on MCP fast, and building on it now means your integration keeps working as the ecosystem grows around it instead of being a one-off you rewrite later. It is also plainly early — the tooling is maturing and the field is wide open, so the teams that expose their systems cleanly now are the ones every new agent can reach first.
Related services
Further reading