Strands Agents MCP Server for Amazon Q

This project provides an MCP (Model Context Protocol) server that scrapes the Strands Agents documentation and indexes it in Elasticsearch, making it searchable for Amazon Q Developer.

Architecture

• Documentation Scraper: Python script that crawls the Strands Agents documentation (v1.1.x)
• Elasticsearch Index: Stores scraped documentation with full-text search capabilities
• MCP Server: Provides Amazon Q with access to the indexed documentation
• Docker Compose: Orchestrates all services

Components

1. scraper/ - Documentation scraping logic
2. mcp_server/ - MCP server implementation
3. elasticsearch/ - Elasticsearch configuration
4. docker-compose.yml - Service orchestration

Quick Start

Option 1: FastMCP Web Server (Recommended)

This project now supports web-accessible MCP via FastMCP:

# Install UV if not already installed
curl -LsSf https://astral.sh/uv/install.sh | sh

# Start Elasticsearch
docker-compose up -d elasticsearch

# Run scraper to populate data
uv run scraper

# Start web-accessible MCP server
uv run fastmcp-server

Server Access:

• MCP Endpoint: http://localhost:8000/mcp/
• For AI Clients: Connect any MCP-compatible client
• For Developers: Use FastMCP Client library

UV Commands:

uv sync                    # Install/update dependencies
uv run scraper            # Run scraper directly
uv run fastmcp-server     # Run web MCP server
uv run mcp-server         # Run local MCP server (stdin/stdout)
uv add <package>          # Add new dependency
uv remove <package>       # Remove dependency

Option 2: Using Pre-indexed Data (Docker)

The docker/ directory contains a Docker Compose setup with pre-indexed Strands documentation:

# Navigate to the docker directory
cd docker/

# Start all services (Elasticsearch, Kibana, and data restoration)
docker-compose up -d

# Wait for services to start (about 30-60 seconds)
# Check status
docker-compose ps

# Elasticsearch will be available on port 9200
# Kibana GUI will be available on port 5601

This setup includes:

• Elasticsearch with pre-indexed Strands documentation
• Kibana for data visualization and exploration
• Automatic data restoration from es-data.tar.gz

Option 3: Traditional Setup (Root Directory)

For a fresh setup without pre-indexed data using traditional Python:

# Start Elasticsearch and Kibana (from root directory)
docker-compose up -d

# Then run the scraper to index documentation
python scraper/main.py

Running the Scraper Locally

Using pip

# Install dependencies
pip install -r requirements.txt

# Install Playwright browsers
playwright install chromium

# Run the scraper to index documentation
python scraper/main.py

Using uv

# Install dependencies
uv sync

# Install Playwright browsers
uv run playwright install chromium

# Run the scraper to index documentation
uv run python scraper/main.py

Running the MCP Server Locally

# Run the MCP server
python mcp_server/main.py

# The MCP server will be available on port 8000

Configuration

• Elasticsearch index: strands-agents-docs
• MCP server port: 8000
• Kibana GUI port: 5601
• Documentation source: https://strandsagents.com/latest/documentation/docs/

Viewing Data with Kibana

Kibana provides a web-based GUI for exploring and visualizing your Elasticsearch data:

1. Access Kibana: Open http://localhost:5601 in your browser (no login required)
2. Create Index Pattern:
   • Go to Stack Management → Index Patterns
   • Create a new index pattern with strands-agents-docs
   • Select @timestamp as the time field if available
3. Explore Data:
   • Use Discover to browse and search through scraped documentation
   • Use Dashboard to create visualizations of your data
   • Use Dev Tools to run Elasticsearch queries directly

Quick Data Exploration

• Discover Tab: View all indexed documents with full-text search
• Search: Use the search bar to find specific documentation content
• Filters: Apply filters to narrow down results by fields
• Time Range: Adjust time range to see when documents were indexed

Usage with Amazon Q

Configure Amazon Q to use this MCP server by adding the server endpoint to your MCP configuration. See AMAZON_Q_INTEGRATION.md for detailed instructions.

Usage with Windsurf

Integrate with Windsurf IDE for enhanced development experience with Strands Agents documentation. See WINDSURF_INTEGRATION.md for setup instructions.

Quick Windsurf Setup

# Copy the configuration file
cp windsurf-mcp-config.json ~/.windsurf/mcp-servers.json

# Restart Windsurf to load the MCP server

Development

Using pip

# Install dependencies
pip install -r requirements.txt

# Run scraper manually
ELASTICSEARCH_URL=http://localhost:9200 python scraper/main.py

# Run MCP server manually
ELASTICSEARCH_URL=http://localhost:9200 python mcp_server/main.py

# Run standalone (Python + Docker Elasticsearch)
python run_standalone.py

# Test the setup
python test_setup.py

Using uv

# Install dependencies
uv sync

# Run scraper manually
ELASTICSEARCH_URL=http://localhost:9200 uv run python scraper/main.py

# Run MCP server manually
ELASTICSEARCH_URL=http://localhost:9200 uv run python mcp_server/main.py

# Run standalone (Python + Docker Elasticsearch)
uv run python run_standalone.py

# Test the setup
uv run python test_setup.py

Integration Options

• Amazon Q Developer - See AMAZON_Q_INTEGRATION.md
• Windsurf IDE - See WINDSURF_INTEGRATION.md
• Custom MCP Client - Use the MCP server directly via stdio protocol