What is Model Context Protocol (MCP)?

TL;DR:

Model Context Protocol (MCP) is a standardized protocol that allows AI agents and third-party tools to connect instantly with each other–without any prior acquaintance.

Why MCP matters?

As AI evolves, particularly with the rise of Agentic AI—MCP takes things a step further, transforming how integrations are handled. It cuts through complexity and puts the entire universe of external tools right at the fingertips of Agentic AI.

Large Language Models (LLMs) on their own are pretty limited—they’re essentially black boxes that take input and (hopefully) return something useful. Without a client wrapped around them, they can’t do much. That’s where AI agents come in (check out our blog: Agentic AI Explained: Workflows vs Agents).

Agents layer in reasoning, planning, and action—adding true autonomy. But for that autonomy to matter, agents need smooth, reliable access to external tools—and that’s exactly the gap MCP bridges.

Pre-MCP chaos: The current situation—soon to be history

Prior to MCP’s release on November 25, 2024, integrating AI agents with external tools was a major pain point. Every connection between an agent and a third-party service was time-consuming and manual.

If an agent needed to connect to 300 different APIs, each would require its own custom implementation, and thus, each agent’s developers would be responsible for engineering connectivity 300 times. This was a massive burden for developers and made it nearly impossible for smaller or lesser-known tools and services to be accessible through agents.

An analogy

Imagine you’re traveling the world, staying at a new hotel each night. Every hotel has a different type of power outlet, and none of them fits your devices, which use a plug no one else supports. That’s what AI-to-service integration looked like before MCP—fragmented, frustrating, and fundamentally broken.

While other approaches were being explored, MCP is clearly emerging as the most practical, scalable, and widely adopted solution.

Introduction of MCP

Enter MCP, the game-changing protocol that rewrites how AI agents connect to external services. Using the outlet analogy, it’s like getting every hotel (agent) and every traveler (service) to agree on a single universal adapter. Suddenly, everything just fits.

This means agents that implement the Model Context Protocol as a client can now connect without any extra effort to every service that implements the MCP server protocol. In other words, once an agent is wired up to talk to one MCP server, it’s effectively ready to speak to hundreds more with almost no extra effort. MCP aims to be the “one Adapter Pattern to rule them all” when it comes to linking AI with third-party services.

Under the hood

For the service owners, MCP is pretty simple—it’s just a wrapper or proxy for your service. In practice, you create a “tool” for every bit of functionality you want to expose to AI, add sufficient comments so an LLM knows how to use it, and point to methods/endpoints that execute that functionality.

For a comprehensive, hands-on guide to building your own MCP server, check out the official MCP Server Quickstart Tutorial.

Example in Python

Here’s how you might write an MCP server for your fancy service:

fancy_mcp = FastMCP("Fancy Service")

@workflow_mcp.tool()
async def create_fancy_things(fancy_definition: Dict[str, Any]) -> str:
   """Creates fancy things using a fancy service based on the provided fancy definition”””
    return fancy_service.call()

Once your service is wrapped or proxied by your MCP server, you can provide the following code and configuration to any agentic AI implementing an MCP client:

{
 "mcpServers": {
     "weather": {
         "command": "uv",
         "args": [
             "--directory",
             "/ABSOLUTE/PATH/TO/PARENT/FOLDER/fancy",
             "run",
             "fancy.py"
         ]
     }
 }
}

This tells the agent how to run the MCP server and to interface with it. Once the server runs, it exposes a ListTools endpoint, letting the agent know what tools are available and how to use them–via docstrings. The agent then passes this information to its models, which, upon receiving a query/instruction, will request whichever tool it deems useful be invoked.

MCP isn’t just about invoking tools—it’s about delivering context. True to its name, MCP offers a standardized way to pipe all sorts of supporting information into an AI agent. That includes:

• Resources
• Prompts
• Sampling

And likely more as the spec evolves.

This is just a high-level overview. For the full deep dive, check out the official MCP documentation and the SDKs for hands-on examples and advanced capabilities.

What’s next?

MCP is the integration layer the developers have been waiting for—simple, scalable, and built for the agentic era. No more custom wrappers or one-off hacks. Just clean, universal connectivity between AI agents and tools.

And with an orchestration platform like Orkes Conductor, things get even better. Imagine orchestrating entire agentic workflows using MCP-powered tools—fully managed, observable, and production-ready. MCP support in Conductor is just around the corner, and it will unlock next-level orchestration for AI systems. Stay tuned.

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Orkes Conductor is an enterprise-grade orchestration platform for process automation, API and microservices orchestration, agentic workflows, and more. Check out the full set of features, or try it yourself using our free Developer Playground.