MCP server development and integration
A custom MCP server gives compatible AI applications a standard way to discover and use your systems. You define the tools and data once instead of building a separate integration for every application.
We build new local or remote MCP servers, connect them to your existing systems and test them with the AI clients that will use them. We also review and improve servers already in use.
What is an MCP server?
Model Context Protocol (MCP) is an open protocol, built on JSON-RPC messages, for connecting language model applications to external tools and information. The AI application a person uses (the host) connects to an MCP server through an MCP client. The server lists what it offers and turns permitted requests into calls to company systems. It can offer three kinds of capabilities.
Tools
Functions the model can call, such as searching a database, creating a ticket, running a calculation or updating a record.
Resources
Information the application can read and place in context, such as a file, database schema, product record or API documentation.
Prompts
Reusable instruction templates that help users start a known task with the correct inputs and context.
What an MCP project can include
Scope depends on the systems being exposed and the clients that need to connect.
Server design
Decide which tools, resources and prompts the server offers, how they are named, what their schemas and errors look like and where its capabilities stop.
System integration
Connect the server to REST or GraphQL APIs and to databases such as PostgreSQL, MySQL or SQL Server. Document libraries, CRM, ERP and ticketing software, cloud storage, source code and CI systems, queues and internal services connect the same way.
Local and remote deployment
Use stdio for a local process or deploy a remote server over Streamable HTTP.
Client integration and testing
Configure the server for the host applications or model APIs that will use it. Tool selection, failures and timeouts are tested with each one.
When it's a good fit
Not every API needs an MCP interface. Whether it is worth adding mostly depends on how many clients will reuse the integration.
MCP is a good fit
Several AI assistants or agents need the same company capability, or users choose which tools and data sources to connect. It also fits when tools have to be discoverable at runtime, when the integration must work with more than one model provider or host, or when the tool set will be maintained as a separate service.
A direct integration may be simpler
One application calls one fixed API operation and the model never has to discover or choose the function. The application already handles authentication and runs the tools itself, and no other client is expected to reuse the integration.
MCP security and permissions
An MCP tool can read sensitive information or change another system. The server checks every request itself, whatever the model has asked for. Tool descriptions and model instructions do not replace access control.
Authentication
Remote HTTP servers use the OAuth-based flow in the MCP authorization specification, while local stdio servers usually take credentials from their environment. Scopes and tenant separation depend on whether a connection represents a user, an application or an internal service.
Token handling
Access tokens are validated for the intended server and are not passed through to unrelated systems.
Least privilege and approvals
Each tool gets only the permissions its own operation needs, and actions that change data can follow different approval rules than reads.
Input and output checks
The server validates arguments, limits returned data and rejects values outside the expected schema.
Prompt injection controls
Tool output is treated as data, and untrusted text cannot gain permission to call another tool.
Audit records
Logs can record the user, tool, arguments, result, approval and timing without copying sensitive data they don't need.
What you receive
- Source code for a working MCP server, with typed tool and resource schemas
- Automated tests for protocol and business behavior
- Authentication and authorization configuration
- A container or local installation package, with client configuration examples
- Documentation of the connected systems and the permissions each tool needs
Frequently asked questions
Can an MCP server connect to our existing API?
Yes. Your API stays as it is, and the MCP server sits in front of it, giving the AI a smaller set of actions it can use.
Can an MCP server connect directly to a database?
It can, but open access to a whole database is rarely a good idea. A few specific operations, limited to what the user is allowed to see, are safer.
Can an MCP server run inside our own network?
It can run on a single computer, inside your own network or in a private cloud. Whether an outside AI application can reach it depends on how that application connects.
Which AI applications can use our MCP server?
AI applications support MCP to different degrees, so not all of them will work the same way. We pick the target applications early and test with each of them.
Is MCP secure?
Not by itself. MCP defines how the AI application and the server talk to each other. Who can do what still has to be set up in the server and in the connected systems.
How long does it take to build an MCP server?
A small server that only reads from one well-documented system can be quick. One that connects several systems and can make changes takes longer. We estimate after looking at what it needs to do.
Can you review and improve an MCP server we already have?
We can. We review how it works, how secure it is and how closely it follows the current MCP specification, then fix the problems or give you a written list of them.
Protocol references
We build against the current MCP specification and the documentation of the clients included in the project.
Work with us
Tell us in a few lines what you're working on. We'll get back to you.
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