Steps to connect AI Agents to your application via the Model Context Protocol (MCP)

It’s been over a year since the Model Context Protocol (MCP) was introduced as a way to integrate AI. MCP enables you to connect AI Agents and LLMs to perform actions on your system or fetch data that can be analyzed or used by AI.

MCP unlocks a lot of potential for automation as you can now initiate operations by giving instructions in natural language.

The MCP protocol, though in it’s infancy, has already seen massive adoption from the industry as a way to supercharge AI Agents with capabilities that do not come built-in and grant them access to information that may be siloed behind closed off or legacy systems.

Startups and nimble teams are well positioned to take advantage of this new, exciting technology just-in-time and in lock-step with the rest of the world. Leveraging AI and building MCP integrations gives you the opportunity to innovate as the best-practices and techniques for MCP are still evolving.

Below are the steps we recommend in integrating MCP in your application.

Five steps to leveraging MCP in the real world

1. Add an MCP Server to your existing system in a development sandbox - Develop or ask an AI agent to create project to implement an MCP server with tools based on functionality in your existing codebase. During this step, you will use libraries or frameworks that enable you to build MCP Servers; define Tools, Prompts and Resources that your application will make available to AI Agents. You can then deploy this MCP server inside your infrastructure, or run it locally on your development environment. The easiest way to get started is to map a functionality or API method 1:1 to an MCP Tool. We recommend exploring by first implementing a few key tools for key workflows, use cases or system functionalities that deliver the most value.

The MCP Quickstart is a good resource: https://modelcontextprotocol.io/quickstart/client

2. Use your MCP Server from an AI Agent tool like Claude, Codex or OpenCode - Once you have built a few MCP tools, the next step is to test your implementation against a real AI Agent to complete real tasks. This step usually involves registering your MCP server in your agent of choice. Once an agent is able to use your MCP server, it will be able to automate steps and use your defined tools to complete a request or fetch information. LLMs are increasingly getting better at knowing how/when to compose tools from an MCP to fulfil a request or complete a task. Try asking for something that exercises that capability.

3. Decide on the scope of the MCP tools and agent authorization - once you have tasted how it feels to supercharge your pipelines by automating via MCP tools orchestrated by an AI Agent. We recommend you take time to reflect on what you want Agents to be capable of doing on your and ensuring each tool call is appropriately scoped and authorized. Agents are just another user and the industry is still exploring solutions around AI Agent identity and auditing.

4. Identify a champion or stakeholder to pilot MCP adoption in production - Having tested and played with MCP implementation, defining the scope and evaluating access issues. Identify a champion or stakeholder to pilot the implementation of MCP in a live-production system to automate workflows where the natural language interface of Agents can help users ease their work.

5. Share your experience - the landscape around AI Integrations, MCPs and access control for AI Agents is rapidly shifting and there are conversations that could use your input. Talking with fellow practitioners is a good way to understand emerging best practices, especially with such new technology and interaction patterns. You can also write and share about your experience on social media or share with us at sales@nndi.cloud.