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tonykipkemboi/crewai-mcp-demo

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This repository demonstrates for using CrewAI with MCP (Model Context Protocol) to interact with external tools and services through a standardized protocol.

Overview

This repository demonstrates for using CrewAI with MCP (Model Context Protocol) to interact with external tools and services through a standardized protocol.

README

CrewAI MCP Demo

This repository demonstrates different approaches for using CrewAI with MCP (Model Context Protocol) to interact with external tools and services through a standardized protocol.

🌟 Featured Examples

🏭 Enterprise Regulatory Intelligence

📍 Location: scaffolding_approach_examples/snowflake_mcp_demo/

A production-ready 3-agent system that combines CrewAI’s multi-agent orchestration with Snowflake’s enterprise data capabilities to build regulatory intelligence systems.

Key Features:

  • 🤖 3-Agent Coordination: Regulatory Intelligence + SEC Filing Analysis + Market News
  • 📊 Enterprise Scale: Semantic search across 372,299 SEC filings via Snowflake MCP
  • 🎯 Professional Output: Investment-grade regulatory impact reports
  • 🚀 Production Ready: Complete deployment and configuration examples

→ View Detailed Tutorial


🚀 Two Integration Approaches

This repository demonstrates two different ways to integrate CrewAI with MCP:

1. 🏗️ Scaffolding Approach (Recommended for Production)

  • Uses CrewAI’s project scaffolding with @CrewBase decorator
  • Structured project with configuration files
  • Built-in get_mcp_tools() method (CrewAI 0.134.0+)
  • Better for complex, maintainable applications

Examples:

  • Snowflake Enterprise Demo - Regulatory intelligence system
  • Mathematician Project - Math operations via local StdIO server
  • Context7 Integration - Document search via streamable-HTTP

2. 📝 Script Approach (Great for Learning/Prototyping)

  • Standalone Python scripts using MCPServerAdapter directly
  • Quick to set up and experiment with
  • Perfect for understanding MCP concepts

Examples:

  • stdio_client_demo.py - Math operations
  • sse_client_demo.py - Cloudflare docs search
  • streamable_http_client_demo.py - Simple greeting
  • multiple_servers_client_demo.py - Combined functionality

🔧 Transport Mechanisms

Streamable HTTP ⭐ (Recommended)

  • Use case: Remote hosted servers (Context7, cloud services)
  • Benefits: Simple setup, bidirectional communication, web-friendly
  • Example: Snowflake MCP, Context7 API

Server-Sent Events (SSE)

  • Use case: Real-time server-to-client updates
  • Benefits: Good for streaming responses, widely supported
  • Example: Cloudflare docs, real-time data feeds

Standard Input/Output (StdIO)

  • Use case: Local development, testing, process-based servers
  • Benefits: Simple local setup, no network required
  • Example: Local math server, file processing tools

📁 Project Structure

crewai-mcp-demo/
├── 🏗️ scaffolding_approach_examples/
│   ├── snowflake_mcp_demo/          # 🌟 FEATURED: Enterprise regulatory intelligence
│   ├── mathematician_project/       # Math operations via local StdIO server
│   └── crewai_context7_mcp/         # Context7 integration via streamable-HTTP
├── 📝 script_approach_examples/      # Standalone script examples
│   ├── stdio_client_demo.py         # Math operations via StdIO
│   ├── sse_client_demo.py           # Cloudflare docs via SSE
│   ├── streamable_http_client_demo.py # Greeting via HTTP
│   └── multiple_servers_client_demo.py # Multiple servers example
├── 🖥️ servers/                       # Local MCP servers
│   ├── hello_http_server.py         # HTTP greeting server
│   └── math_stdio_server.py         # StdIO math server
└── README.md                        # This file

⚡ Quick Start

Option 1: Try the Enterprise Demo (Recommended)

cd scaffolding_approach_examples/snowflake_mcp_demo/
# Follow the detailed setup guide in that directory

Option 2: Try a Simple Script

# For math operations (local server)
python3 script_approach_examples/stdio_client_demo.py

# For Cloudflare docs (remote server)  
python3 script_approach_examples/sse_client_demo.py

🛠️ Prerequisites

  • Python: Version >= 3.10 < 3.14
  • CrewAI: Version >= 0.134.0 (for scaffolding approach)
  • API Keys: Depends on the example (OpenAI, Snowflake, etc.)
  • uv (recommended) or pip for package management

📚 Learn More

Featured Documentation

Resources


🤝 Contributing

Contributions welcome! Areas where we’d love help:

  • Additional MCP server examples
  • More transport mechanism demos
  • Documentation improvements
  • Bug fixes and optimizations

📄 License

This project is licensed under the MIT License - see the LICENSE.md file for details.


⭐ Star History

Happy building with CrewAI and MCP! 🚀

View the full README on GitHub

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