MCP servers
Give AI clients a safe, typed door into your system.
Model Context Protocol servers with proper auth, tenant isolation and tool schemas that describe what they actually do. Built as a hosted multi-tenant service or shipped for your own infrastructure.
Project pricing
From $2,000
That covers one MCP server over one system: a set of read tools, auth, tenant isolation and schemas, shipped for your infrastructure. Write tools, hosted multi-tenant delivery and a public listing run to $7,750.
Send the brief and get a number back.
Describe the projectEveryone wants your data in their AI client
The requests arrive one at a time. Someone wants your catalogue inside their assistant, someone else wants the reporting numbers where they already work, and the answer so far has been an export or a bespoke integration. Meanwhile the shortcut is worse than the wait: a personal API token pasted into a desktop client, with no scope, no tenant boundary and no way to revoke it for one person. An MCP server is the same integration built once, with the door and the lock designed together, instead of a key handed out and then forgotten.
What gets built
Tools that describe what they actually do
A schema per tool with typed arguments, honest descriptions and examples, because the client reads that schema to decide when to call. A vague description is a tool that gets called at the wrong moment and blamed for the answer.
Auth and tenant isolation at the boundary
Identity resolved before any tool runs, every query scoped to that tenant, and scopes narrow enough that a compromised token is a small problem. Nothing depends on the client behaving well.
Deployment you can run
Shipped for your own infrastructure or hosted as a multi-tenant service, with logs per call, rate limits per tenant, and a version policy so a schema change does not break somebody's client on a Friday.
Why it is built this way
The schema is the product
An MCP client is not a person reading documentation; it decides from the tool description alone. Precise names, tight arguments and an honest description of the failure cases do more for adoption than any amount of implementation polish behind them.
Read before write
The first version exposes reads. Write tools arrive after the read surface has been used in anger, because a mistaken read is a wrong answer and a mistaken write is your data. That order costs nothing and removes the worst outcome.
How it runs
A read of what the AI clients should be able to do, and the sharper question underneath it: what they must never do, and which of your existing endpoints is not safe to expose as it stands.
The tool surface designed on paper first: names, arguments, return shapes, error cases and the scope each tool requires, reviewed before anything is implemented.
The server built over your existing API or database, with auth, tenant scoping, rate limits and structured logging per call.
Tested from a real client, not only from a test harness, because the interesting failures are in how a model chooses and chains tools rather than in the responses themselves.
Deployment your way, with a short document for the people who will connect to it and a version policy for the schema.
Background
What MCP changed, and what it did not
One integration instead of one per client
Before a shared protocol, every assistant that wanted your data needed its own connector. A single MCP server turns that into one implementation with many consumers, which is the same economics that made a public API worth building in the first place.
Tool descriptions are the new API documentation
The consumer is a model choosing between tools from their descriptions alone. Names and descriptions stop being documentation and become behaviour: rename a tool and the pattern of calls changes.
Authorisation is the part that gets skipped
The quickest path is a long-lived token with broad scope pasted into a client on somebody's laptop. It works immediately and it is exactly the thing that cannot be revoked cleanly, audited per user, or scoped to one tenant later.
Servers are judged on the boring parts
Adoption follows reliability, clear errors and a schema that does not change under people. The interesting engineering is in isolation, limits and versioning, not in the tools themselves.
Describe the project
Tell us what you have and what should change. Within two working days you get a written calculation: the scope broken into parts with a price against each, or the questions needed to write one. No discovery call in between.
Questions
MCP work, asked and answered
It is if AI clients are already a channel for you, because the alternative is one bespoke integration per client. This studio runs a public MCP server for its own API in production, and the answer here comes from operating one rather than from reading the specification.
Usually yes, as the layer underneath. The MCP server sits on top and exposes a smaller, blunter surface, because tools designed for a model are not the same shape as endpoints designed for a developer.
Read-only tools first, narrow scopes, and any write tool designed with an explicit confirmation and a reversible operation. The constraint lives in the server, not in the prompt, because the prompt belongs to whoever is running the client.
Both are built the same way and the choice is about who carries the operational load and where the data may sit. Self-hosted suits a single tenant with strict data rules; hosted suits offering the server to your own customers.
It still moves, so the server is written with the transport and the protocol version isolated from your tool logic. An upgrade is then a small, tested change instead of a rewrite.