MCP-on-AWS-Bedrock
Language:
Python
Stars:
35
Forks:
8
MCP on AWS Bedrock
A simple and clear example for implementation and understanding Anthropic MCP (on AWS Bedrock).
Overview
This project demonstrates how to implement and use Anthropic's Model Context Protocol (MCP) with AWS Bedrock. It provides a client implementation that can interact with MCP-enabled tools through AWS Bedrock's runtime service.
Prerequisites
- Python 3.10 or higher
- AWS account with Bedrock access
- Configured AWS credentials
- UV package manager
Project Structure
client_stdio.py
: Main client implementation for interacting with Bedrock and MCP tools using stdio modeclient_sse.py
: Main client implementation for interacting with Bedrock and MCP tools using sse modemcp_simple_tool/
: Directory containing the MCP tool implementationserver.py
: MCP tool server implementation__main__.py
: Entry point for the tool
pyproject.toml
: Project dependencies and configuration
Usage
Run the stdio client with:
uv pip install boto3
uv run client_stdio.py
The client will:
- Initialize a connection to AWS Bedrock
- Start the MCP tool server
- List available tools and convert them to the format required by Bedrock
- Handle communication between Bedrock and the MCP tools
Run the sse client with:
# server
uv pip install boto3 uvicorn
uv run mcp-simple-tool --transport sse --port 8000
# client
uv run client_sse.py
Features
- Seamless integration with AWS Bedrock runtime
- Tool format conversion for Bedrock compatibility
- Asynchronous communication handling
- Structured logging for debugging
Contributing
Feel free to submit issues and pull requests to improve the implementation.
License
MIT License
References
-
- [AWS Bedrock](https://aws.amazon.com/bedrock/)
Publisher info
More MCP servers built with Python
Create, backtest, and execute trades directly in one chat box. The Composer MCP Server gives LLMs the power to backtest investment ideas and execute automated trading strategies. Trade across stocks, ETFs, and crypto directly in Claude.
An MCP to generate presentations with AI. Create and edit PowerPoint presentations with AI.
The PaddleOCR MCP server brings enterprise-grade OCR and document parsing capabilities to AI applications. Built on PaddleOCR — a proven solution with 50,000+ GitHub stars, deeply integrated by leading projects like MinerU, RAGFlow, and OmniParser— with targeted optimizations based on the MCP concept.