Claude, Cursor, or a client's internal agent stack—to trigger actions in your product on a user's behalf. If your goal is simply to add an AI feature inside your own app, skip the MCP overhead and just use a standard LLM API integration. It's faster and usually cheaper.
The Model Context Protocol (MCP) is essentially a thin adapter. It takes your existing product actions—things like "create invoice" or "check order status"—and exposes them as tools in a schema that an agent can read and reason about. It doesn't grant your software new powers; it just gives external agents a standardized way to access the capabilities you already have.
When to actually deploy MCP #
In my experience rolling out AI tools at work, we hit this exact crossroads with our outreach engine. A client asked if they could point their own internal agent stack at our tool instead of using our UI. That's the litmus test: does the user want to interact with your product as a tool within their own agent, rather than as a standalone website?
I see three real-world scenarios where this is a requirement:
Technical B2B Sales: If your buyers are already running their own agent workflows and asking for API connectivity for their agents, an MCP server turns a "custom integration" conversation into a "plug-and-play" win.Agent Orchestration: If your product acts as a layer that needs to expose sub-tools to a supervising agent.Developer Platforms: When "AI-native integration" is a core part of your value proposition.
For everyone else, a well-documented REST API with an OpenAPI spec is often enough, as most agent frameworks can already consume those.
The implementation cost #
If you have 5-10 core actions, building an MCP server is a small API project, not a rewrite. It typically takes one engineer a few weeks, provided your backend logic is clean. The real time-sink isn't the protocol; it's the tool design. We learned this the hard way when we tried to collapse a two-step "extract facts, then draft email" flow into a single call. Figuring out the right granularity for what to expose to the agent takes far more thought than writing the actual adapter code.
The workload generally splits into:
Tool design: Determining which actions to expose and how granular they should be.Schema definition: Mapping those actions into the MCP format.
Next Azure Linux VM: A Practical Tutorial →