aws-strands-mcp-knowledge-graph

vAirpower/aws-strands-mcp-knowledge-graph

3.2

If you are the rightful owner of aws-strands-mcp-knowledge-graph and would like to certify it and/or have it hosted online, please leave a comment on the right or send an email to henry@mcphub.com.

The Model Context Protocol (MCP) server is a specialized server designed to facilitate communication and data exchange between various components in a knowledge graph ecosystem, particularly focusing on SPARQL queries and semantic search capabilities.

Tools
7
Resources
0
Prompts
0

AWS Strands Agent with MCP Knowledge Graph Proof of Concept (Text-to-SPARQL/STARDOG)

Screenshot 2025-07-29 at 4 50 29 PM

Python Streamlit AWS Bedrock MCP License: MIT

PoC-ready Streamlit application featuring AWS Strands Agents, Model Context Protocol (MCP) server integration, and interactive knowledge graph visualization with Claude 3.7 Sonnet.

🌟 Features

  • 🤖 AWS Strands Agent: Autonomous reasoning with Claude 3.7 Sonnet via AWS Bedrock
  • 📊 Interactive Knowledge Graph: Real-time graph visualization with streamlit-agraph
  • 🔗 MCP Integration: 7 specialized tools for SPARQL queries and semantic search
  • ⚡ In-Memory RDF Store: 80+ triples of GEOINT data for demo purposes
  • 🎯 One-Command Launch: Single script starts both MCP server and Streamlit app
  • 🔐 Security First: Environment-based configuration with no hardcoded credentials

🚀 Quick Start

Prerequisites

  • Python 3.9 or higher
  • AWS Account with Bedrock access
  • AWS CLI configured with appropriate permissions

Installation

  1. Clone the repository

    git clone https://github.com/vAirpower/aws-strands-mcp-knowledge-graph.git
cd aws-strands-mcp-knowledge-graph

  2. Set up environment

    python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate
pip install -r requirements.txt

  3. Configure AWS credentials

    # Option 1: AWS CLI
aws configure

# Option 2: Environment variables
export AWS_ACCESS_KEY_ID=your_key_here
export AWS_SECRET_ACCESS_KEY=your_secret_here
export AWS_REGION=us-east-1

# Option 3: Copy and edit environment file
cp .env.example .env
# Edit .env with your actual AWS credentials

  4. Launch the application

    python run_demo.py

  5. Access the demo

💬 Sample Queries

Try these example queries in the Streamlit interface:

  • "What facilities are near Washington DC?"
  • "Find all military bases in Virginia"
  • "Tell me about the Pentagon"
  • "Show me airports and their locations"
  • "What types of facilities do we have data for?"

🏗️ Architecture

The application consists of three main components:

┌─────────────────────┐    HTTP    ┌─────────────────────┐
│   Streamlit App     │◄──────────►│   MCP Server        │
│   (Port 8501)       │            │   (Port 8000)       │
└─────────────────────┘            └─────────────────────┘
           │                                  │
           │                                  │
           ▼                                  ▼
┌─────────────────────┐            ┌─────────────────────┐
│   AWS Strands       │            │   In-Memory RDF     │
│   Agent             │            │   Store (80 triples)│
│   (Claude 3.7)      │            │                     │
└─────────────────────┘            └─────────────────────┘
           │
           ▼
┌─────────────────────┐
│   AWS Bedrock       │
│   API               │
└─────────────────────┘

Component Details

  • Streamlit App (app.py): Interactive web interface with graph visualization
  • MCP Server (standalone_server.py): HTTP server providing 7 specialized tools
  • Strands Agent (strands_agent.py): AWS Bedrock integration with Claude 3.7 Sonnet
  • Graph Visualizer (graph_visualizer.py): Interactive network graph rendering
  • MCP Client (mcp_http_client.py): HTTP client for MCP communication

🛠️ Available MCP Tools

The MCP server provides 7 specialized tools:

  1. execute_sparql: Execute SPARQL queries against the RDF store
  2. get_facilities_near: Find facilities within proximity of coordinates
  3. search_by_text: Text-based search across facility descriptions
  4. get_sample_data: Retrieve sample RDF data for exploration
  5. get_classes: List all RDF classes in the knowledge graph
  6. get_properties: List all RDF properties in the knowledge graph
  7. count_triples: Get total count of RDF triples

📁 Project Structure

aws-strands-mcp-knowledge-graph/
├── README.md                 # This file
├── LICENSE                   # MIT license
├── .gitignore               # Git ignore rules
├── .env.example             # Environment template
├── requirements.txt         # Python dependencies
├── run_demo.py             # Main launcher script
├── app.py                  # Streamlit application
├── standalone_server.py    # MCP server
├── strands_agent.py        # AWS Strands Agent
├── mcp_http_client.py      # MCP HTTP client
└── graph_visualizer.py     # Graph visualization

⚙️ Configuration

Environment Variables

Copy .env.example to .env and configure:

# AWS Configuration
AWS_REGION=us-east-1
AWS_ACCESS_KEY_ID=your_aws_access_key_here
AWS_SECRET_ACCESS_KEY=your_aws_secret_key_here

# MCP Server Configuration
MCP_SERVER_URL=http://localhost:8000
MCP_SERVER_PORT=8000

# Streamlit Configuration
STREAMLIT_PORT=8501
STREAMLIT_HOST=localhost

# Agent Configuration
BEDROCK_MODEL_ID=anthropic.claude-3-7-sonnet-20250219-v1:0
AGENT_MAX_ITERATIONS=10
AGENT_TIMEOUT=30

AWS Bedrock Setup

  1. Enable Claude 3.7 Sonnet in your AWS Bedrock console
  2. Set up IAM permissions for Bedrock access: 
    {
  "Version": "2012-10-17",
  "Statement": [
    {
      "Effect": "Allow",
      "Action": [
        "bedrock:InvokeModel",
        "bedrock:InvokeModelWithResponseStream"
      ],
      "Resource": "arn:aws:bedrock:*::foundation-model/anthropic.claude-3-7-sonnet-*"
    }
  ]
}

🔧 Development

Running Tests

# Install test dependencies
pip install pytest pytest-cov

# Run tests
pytest tests/ -v --cov=.

Code Formatting

# Install formatting tools
pip install black isort mypy

# Format code
black .
isort .

# Type checking
mypy . --ignore-missing-imports

Local Development

# Run MCP server only
python standalone_server.py

# Run Streamlit app only (requires MCP server running)
streamlit run app.py --server.port 8501

🐳 Docker Support

Build and Run

# Build image
docker build -t aws-strands-mcp .

# Run container
docker run -p 8501:8501 -p 8000:8000 \
  -e AWS_ACCESS_KEY_ID=your_key \
  -e AWS_SECRET_ACCESS_KEY=your_secret \
  -e AWS_REGION=us-east-1 \
  aws-strands-mcp

Docker Compose

# Start all services
docker-compose up -d

# View logs
docker-compose logs -f

# Stop services
docker-compose down

📊 Knowledge Graph Data

The demo includes 80+ RDF triples representing GEOINT facilities:

  • Military Bases: Pentagon, Joint Base Andrews, etc.
  • Airports: Reagan National, Dulles International, etc.
  • Government Facilities: White House, Capitol Building, etc.
  • Geographic Coordinates: Latitude/longitude for all facilities
  • Relationships: Facility types, locations, and connections

Sample RDF Data

@prefix geo: <http://www.w3.org/2003/01/geo/wgs84_pos#> .
@prefix facility: <http://example.org/facility/> .

facility:pentagon a facility:MilitaryBase ;
    rdfs:label "Pentagon" ;
    geo:lat 38.8719 ;
    geo:long -77.0563 ;
    facility:location "Arlington, Virginia" .

🔍 Troubleshooting

Common Issues

  1. AWS Credentials Not Found

    # Verify AWS credentials
aws sts get-caller-identity

# Or check environment variables
echo $AWS_ACCESS_KEY_ID

  2. MCP Server Connection Failed

    # Check MCP server health
curl http://localhost:8000/health

# Restart MCP server
python standalone_server.py

  3. Streamlit App Won't Start

    # Check port availability
lsof -i :8501

# Run with different port
streamlit run app.py --server.port 8502

  4. Graph Visualization Issues

    # Clear browser cache
# Refresh page
# Check browser console for errors

Debug Mode

Enable debug logging:

export LOG_LEVEL=DEBUG
export DEBUG_MODE=true
python run_demo.py

🤝 Contributing

We welcome contributions! Please see our for details.

Development Setup

  1. Fork the repository
  2. Create a feature branch: git checkout -b feature/amazing-feature
  3. Make your changes
  4. Run tests: pytest tests/
  5. Format code: black . && isort .
  6. Commit changes: git commit -m 'Add amazing feature'
  7. Push to branch: git push origin feature/amazing-feature
  8. Open a Pull Request

📄 License

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

🙏 Acknowledgments

  • AWS Bedrock team for Claude 3.7 Sonnet integration
  • Model Context Protocol for the MCP specification
  • Streamlit community for the visualization framework
  • Open source contributors who made this project possible

📞 Support

🔗 Related Projects

⭐ Star this repo if you find it useful!

Made with ❤️ by Adam Bluhm