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MCP development services

MCP Development: Custom MCP Servers for AI That Works Inside Your Systems

We build custom Model Context Protocol servers that let AI assistants read your data and take real actions in your CRM, ERP, databases and internal tools, within limits you set and with a log of everything they do.

Book an MCP scoping callSee what we have shipped

What an MCP server actually is

The Model Context Protocol (MCP) is an open standard for connecting AI assistants to the tools and data behind a business. An MCP server sits in front of one of your systems and exposes a short list of named tools, such as “look up an invoice” or “create a ticket”, that any MCP-compatible assistant can call.

Without one, an assistant knows only what you paste into the chat. With a well-built one, it answers from live records and does routine work, while the server decides what it may touch.

The protocol is the easy part. The engineering is choosing which tools to expose, what each one is allowed to change, and what happens when the assistant gets it wrong.

What we build

From a single read-only server to the agent workflow that uses it.

Servers over your own systems

A server in front of your APIs, databases and line-of-business software, shaped around the questions your team really asks rather than a copy of your schema.

Servers for the SaaS tools you pay for

Where a vendor’s own server is missing, too broad or read-only, we build one scoped to your workflow across CRM, helpdesk, billing and messaging.

Agent workflows on top

Assistants and agents that use your servers to carry a whole process, with a person approving anything that cannot be undone.

Remote servers with real authentication

Hosted servers with sign-in, per-user permissions and rate limits, so a whole team can connect their own assistants safely.

Tests that call tools like an assistant would

Evaluation suites that exercise your tools the way a model does, so rewording a tool description cannot quietly change what the assistant does.

Hardening and handover

Documentation, monitoring and a runbook, so your team can run and extend the server once we step back.

Built so the assistant cannot do more than you meant

Most failures come from nobody deciding what the assistant may do on its own. We decide that first, and enforce it in the server rather than in a prompt.

  1. 01

    Read-only by default

    A server starts with read tools. Write tools are added one at a time, each with its own scope. On our Meridian Console build, writes are refused at the transport, not merely discouraged in instructions.

  2. 02

    Scoped tools, not a master key

    Each tool does one job with the minimum permission it needs in the underlying system. Credentials stay on the server and never reach the model.

  3. 03

    A person signs off the irreversible

    Payments, deletions and outbound messages wait for human approval. The assistant prepares the action; a person confirms it.

  4. 04

    Every call is logged

    Tool, arguments, caller and result are recorded, so “what did it do, and why?” always has an answer.

  5. 05

    Inputs are treated as untrusted

    Tickets, emails and documents can contain instructions aimed at the model. Arguments are validated on the server, and nothing a tool returns can widen what the assistant is allowed to do.

MCP servers we have shipped

Both are part of larger production builds, not demos.

Support operations consoleMeridian ConsoleAn AI assistant over a read-only MCP server that spans four support and finance platforms, with writes refused at the transport.Read case studyGeoTech / data intelligence platformGeoVerdantAn MCP server on a satellite land-analysis platform, so AI assistants can run the same workflow a person does: draw a boundary, get vegetation indices back.Read case study

How an MCP build runs

  1. 01

    Map the work

    About a week. We find the questions and actions an assistant should handle, the systems involved, and what must stay with a person.

  2. 02

    Design the tools

    Names, arguments, scopes and descriptions. Tool design decides whether an assistant uses your server well, so it gets its own step.

  3. 03

    Build and test

    The server, authentication, logging and an evaluation suite, so behaviour is checked rather than assumed.

  4. 04

    Ship small, then widen

    A first server is typically running in production in three to four weeks. We add tools once the first slice has proved itself.

MCP development, answered

It is building the servers that let AI assistants use your systems through the Model Context Protocol. That covers designing the tools, connecting them to your data, adding authentication and permissions, and testing how assistants actually behave against them.

An API is built for code written by developers. An MCP server wraps your APIs in a small set of described tools an AI assistant can choose between, with permissions and logging around them. It usually sits in front of your existing API and does not replace it.

Any client that supports MCP, including Claude and a growing list of editors and agent frameworks. We confirm compatibility with the clients you plan to use during the first week.

It is as safe as the boundaries around it. We start read-only, scope every tool, keep credentials server-side, require sign-off for irreversible actions and log every call. The server enforces these limits, so they hold even when the assistant misbehaves.

The audit takes about a week and a first server is typically live in three to four weeks. Cost depends on how many systems are involved and how many write actions you need. We scope it after the audit, so you know what you are buying before you commit.

Yes, that is the usual starting point. Where your system has an API we build on it. Where it does not, we build the bridge. Nothing gets migrated.

You do. The server, its tests and its documentation are yours, and your team can run and extend them.
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Read more
Agentic AIYour agent doesn't need a bigger prompt. It needs a boundary.Most agent projects fail in the same place: nobody decided what the agent is allowed to do on its own. That is an engineering decision, not a prompting one.Testing & GuardrailsAn agent you cannot audit is not in production, it is on trialThe second question every operator asks is "what did it do, and why?" If the system cannot answer that, it does not matter how good the demo was.Systems IntegrationYour integration layer is a person with two browser tabs openMost businesses already have an integration layer. It is a human being, and they are the least reliable and most expensive part of the stack.

Tell us which system you want an assistant inside.

Thirty minutes with an engineer. You leave with the list of tools worth exposing and the ones that should stay behind a person, whether you hire us or not.

Book an MCP scoping call