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SERVICE / MCP SERVER DEVELOPMENT

Custom MCP server development that connects AI to your data.

We build Model Context Protocol servers that connect your data feeds, databases, APIs and internal tools to Claude, ChatGPT and other MCP clients, securely and under your control.

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MODEL CONTEXT PROTOCOLCLAUDECHATGPTOAUTHAUDIT LOGSEU HOSTING
01 / OVERVIEW

MCP server development

AI models are only as useful as the data they can reach. The Model Context Protocol (MCP) is an open standard that lets AI applications like Claude, ChatGPT and other MCP clients call tools and read data through one consistent interface. A custom MCP server is the layer that decides what the model can see, what it can do and who is allowed to ask.

We built the MCP server for Asora, a wealth management platform: dozens of domain tools exposing portfolio, reporting and family-office data to AI models, with secure sandboxed processing of large financial datasets. For the technical background, read our article on building a custom MCP server for company data.

02 / MCP

What a custom MCP server does for your company

Instead of copying spreadsheets into a chat window, your team asks questions and the model pulls live answers from the systems that hold the truth. An MCP server wraps your databases, internal APIs, data feeds and SaaS tools as well-described tools and resources that any compatible AI client can use.

Because MCP is a standard, you build the integration once. The same server works with Claude today and with other MCP clients tomorrow, without rewriting integrations for every AI vendor.

  • Databases and data warehouses exposed as typed, queryable tools
  • Internal REST and GraphQL APIs wrapped with business logic
  • Real-time data feeds, reports and documents as resources
  • Write actions such as creating tickets or updating records, only where you allow them
03 / MCP

MCP server security: auth, scoping and audit logs

An MCP server gives an AI model access to your business. We treat it with the same care as any production API handling sensitive data, because that is what it is. Asora handles portfolio and family-office data, so security was never optional.

Large datasets are processed server side in a sandbox. The model receives aggregates, filtered rows and computed results instead of millions of raw records, which is faster, cheaper and safer.

  • Authentication through OAuth or your identity provider, no shared keys
  • Per-user scoping: the model sees only what the person asking may see
  • Read-only tools by default; write tools separated, confirmed and rate limited
  • Audit logs of every tool call, input and result
  • Sandboxed processing of large datasets, so raw data never floods the model context
  • EU hosting on our dedicated hardware in German data centers
04 / MCP

Designing MCP tools that AI models use correctly

A working MCP server is not the same as a useful one. Models choose tools from their names, descriptions and schemas. Too many overlapping tools and the model guesses; too few and it cannot answer. We design tool sets around real questions from your team, with clear schemas, helpful errors and predictable output.

We test every server against real AI clients with evaluation prompts, then refine the tools based on how the model actually uses them. MCP is often part of a broader custom AI solution with agents and self-hosted models, and it can be added to an existing web application.

HOW WE WORK

How we
work.

  1. // 01

    Free roadmap in 24 hours

    Tell us which systems and questions matter. You get a cost breakdown, a timeline projection and a proposed tool set with security boundaries.

  2. // 02

    Data and permission mapping

    We map data sources, user roles and what each role may read or change. Read and write tools are decided explicitly.

  3. // 03

    Build and evaluate

    Tools are built in milestones and tested every week against real AI clients with your actual questions.

  4. // 04

    Secure deployment

    The server goes live on our own EU hardware or your infrastructure, with authentication, audit logs and monitoring switched on.

  5. // 05

    Extend and maintain

    We add tools as new questions come in and keep the server current as the MCP specification and clients evolve.

RELATED WORK

Proof, not
promises.

IRELAND · FINANCE · WITH BLOCSHOP

Asora

Most of the frontend, the Ionic and Angular mobile app, and the custom MCP server and agentic AI architecture for a wealth management platform.

Asora case study
CZECH REPUBLIC · SERVICE DELIVERY

Uniters

Transforming freelancing into a Service Delivery Model: an animated company website and an enterprise contractor management system with a built-in AI agent.

Uniters case study
FAQ

Questions
answered.

A

What is an MCP server, in plain terms?

It is a small, secure service that sits between AI models and your systems. The model asks for a tool, for example "get portfolio performance for this client", and the server checks permissions, runs the query against your data and returns the result. The model never gets direct access to your database.
B

Which AI clients work with a custom MCP server?

Any client that supports the Model Context Protocol, including Claude and ChatGPT, plus IDEs, agent frameworks and your own applications. Because MCP is an open standard, one server serves many clients, and you are not locked into a single AI vendor when the market shifts.
C

How do you keep company data secure?

Every request is authenticated and scoped to the user who made it. Tools are read-only unless write access is explicitly designed, confirmed and logged. Large datasets are processed in a sandbox so only results reach the model. Servers run on EU hardware in German data centers with 24/7 monitoring.
D

How much does custom MCP server development cost?

It depends on scope: the number of systems to connect, how many tools you need, the complexity of permissions and whether write actions are involved. A focused read-only server over one database is a much smaller project than dozens of tools across several platforms. The free roadmap gives you a number.
E

How long does it take to build an MCP server?

A focused first version over one or two data sources can be usable quickly, then grow tool by tool. Larger servers are delivered in milestones with weekly testing against real AI clients. The roadmap includes a timeline projection for each milestone, so you know when each capability lands.
F

Who owns the MCP server?

You do. Source code, tool definitions, evaluation prompts and IP for the work you pay for belong to you. The server can run on your infrastructure or on our dedicated EU hardware, and you can move it at any time. The tools follow the open MCP standard, so no proprietary runtime ties you to us.
RELATED SERVICES

Also
on the table.

ACustom AISelf-hosted AI models, custom AI agents, MCP servers and AI strategy from a team that runs AI in production.BWeb applicationsCustom web apps, dashboards, portals, SaaS and internal tools in Angular, React, Node.js and TypeScript.CConsultingArchitecture reviews, stack choices, estimates and AI strategy, with free tech advice during project estimation.
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