👾 3 Ways to Integrate MCP Servers into AI Agent Workflows

Difference Between MCP and Function Calling Tools

Before the release of the Model Context Protocol, you had to implement tools (or functions) yourself. What’s the innovation? In simple terms:

Function Calling = AI decides what and when to call a tool, calling it directly.

MCP = A universal system that standardizes how tools are discovered, accessed, and managed, acting like a marketplace of AI tools.

This means MCP offers more scalability, flexibility, and easier integration for multiple tools, while normal function calls offer simplicity and speed for straightforward tasks.

No-Code Examples

1. Make.com: You can set up MCP servers and clients in Make. To set up an MCP server, you first need to create an MCP token in your user’s settings. Then paste it in the MCP configuration for your MCP client.

   Your client (e.g., Cursor, Claude Desktop, or programmed agents) can then call any Make scenario you configured to run on demand.
   Example config for Claude Desktop:

{
  "mcpServers": {
    "make": {
      "command": "npx",
      "args": [
        "-y",
        "mcp-remote",
        "https://<MAKE_ZONE>/mcp/api/v1/u/<MCP_TOKEN>/sse"
      ]
    }
  }
}

2. n8n: Supports both MCP server and client nodes. The MCP server node allows n8n to function as an MCP server by exposing workflows as tools accessible to MCP clients.

   The MCP client node enables n8n to connect to external MCP servers, allowing it to use the capabilities of those servers within its workflows.

Advanced Production-Ready Integrations

3. CrewAI: Supports the integration of MCP tools. Install the extra package:

uv pip install 'crewai-tools[mcp]'

Here is an example Python script that integrates the ScrapeGraphAI MCP server in CrewAI for agents to call:

import os
from crewai import Agent
from crewai_tools import MCPServerAdapter
from mcp import StdioServerParameters

# Replace this with your actual ScrapeGraphAI API key
SCRAPEGRAPH_API_KEY = "YOUR-SGAI-API-KEY"

# Define the MCP Stdio server parameters
server_params = StdioServerParameters(
    command="npx",
    args=[
        "-y",
        "@smithery/cli@latest",
        "run",
        "@ScrapeGraphAI/scrapegraph-mcp",
        "--config",
        f'{{"scrapegraphApiKey":"{SCRAPEGRAPH_API_KEY}"}}'
    ],
    env={**os.environ}
)

# Use the MCP tools in a CrewAI agent
with MCPServerAdapter(server_params) as mcp_tools:
    print(f"✅ Loaded tools: {[tool.name for tool in mcp_tools]}")

    agent = Agent(
        role="Web Data Harvester",
        goal="Scrape and analyze web data using ScrapeGraphAI tools.",
        backstory="Expert at leveraging ScrapeGraphAI for data extraction.",
        tools=mcp_tools,
        reasoning=True,
        verbose=True
    )

    # Example usage of the agent (replace with your Crew setup)
    # For example, you could assign this agent a task in a Crew workflow.

In other agent frameworks, the MCP integration works similarly. It’s basically a plug-and-play way to make external software available to your agents.

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