MCP server development

Model Context Protocol (MCP) gives AI clients a standard way to connect to external systems and use approved data and actions. The protocol is open and free to use. The development work lies in connecting it correctly to your systems, deciding which capabilities to expose, and enforcing who can use them.

Under the hood, an MCP server will often wrap existing APIs and present them through the standard interface expected by AI clients. This puts the work in the same category as custom API and system integration development. The difference is on the AI side: each supported client can use the MCP interface instead of requiring a completely separate connector.

An MCP server can serve two distinct product strategies:

• Internal enablement: Let approved AI tools access selected data or actions in your CRM, ERP, PIM, internal database, or legacy system.
• SaaS product feature: Give your customers a supported way to use your product from clients such as Claude, ChatGPT, Grok, Gemini, and Cursor.

Both scenarios depend on the same design decision: the server should expose a useful capability without making the underlying system broadly accessible.

MCP server development is delivered as a Fixed Price Project. Once we have agreed on the connected systems, tools and resources, security boundary, supported AI clients, and deployment requirements, you receive one price for the defined project.

• A custom MCP server connected to the systems and APIs agreed for the project.
• A defined set of tools and resources covering only approved data and actions.
• Authentication and permission scopes for the users and AI clients that connect.
• Testing against the AI clients your team or customers are expected to use.
• Deployment to your environment, with configuration, security, and maintenance documentation.
• Optional upkeep through Continuous Monthly Development as your systems, AI clients, and MCP requirements change.

Connecting an AI client to an API is only the beginning. We define the tools and resources around real roles and tasks, then separate read-only access from actions that change data. A sales user might be allowed to retrieve an account and prepare a note, for example, without receiving access to pricing rules or administrative functions.

Authorization is enforced by the server and the underlying system, rather than relying on instructions given to the AI model. This keeps the source system as the authority for identity, permissions, validation, and approved changes.

Protocol compatibility alone doesn’t show that the full workflow works. Claude, ChatGPT, Grok, Gemini, Cursor, and other clients can differ in setup, approvals, and how they present tools or resources. We test the agreed workflows in the AI clients that matter to the project, including authentication failures, insufficient permissions, malformed requests, and write actions where applicable.

The work follows the same quality discipline as our broader software testing process: requirements are reviewed early, the implementation is tested in staging, and issues are verified before deployment.

1. Map the use cases. Identify the systems, users, AI clients, data, and actions involved.
2. Define the boundary. Agree on tools, resources, authentication, permission scopes, and read or write access.
3. Build and test. Implement the server against the available APIs or data layer, then test the agreed client workflows.
4. Deploy and document. Release the server to your environment and document how it is configured, secured, and maintained.