What are the requirements to run this server?
Python 3.12, AWS credentials with Cost Explorer access, and Anthropic API access. Optionally, Amazon Bedrock access (for LangGraph Agent) and an EC2 instance for remote deployment.
@aws-samples
基本信息
MCP for AWS Cost Explorer and CloudWatch logs
工具列表暂未提供
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工具是从 README 中自动提取的。维护者可以在 ## Tools 标题下列出工具,即可填充这部分内容。
This MCP server provides AWS cost data via Cost Explorer and Amazon Bedrock usage data from CloudWatch model invocation logs, enabling natural language queries through Claude. It is intended for users who want to analyze and visualize their AWS cloud spending interactively.
Install Python 3.12 and uv, clone the repository, set up a virtual environment, and configure AWS credentials. Run server.py with MCP_TRANSPORT=stdio for local use, then add the configuration to Claude Desktop (either via Docker or UV). For remote deployment, set MCP_TRANSPORT=sse, run on EC2, and connect using a CLI client or a Chainlit LangGraph agent.
Python 3.12, AWS credentials with Cost Explorer access, and Anthropic API access. Optionally, Amazon Bedrock access (for LangGraph Agent) and an EC2 instance for remote deployment.
The server queries AWS Cost Explorer and CloudWatch Logs live via API calls. No data is stored locally; all information is fetched on demand.
Yes. Set `MCP_TRANSPORT=sse` and run on EC2. Note that Claude Desktop does not support remote MCP servers; use a LangGraph agent or the provided CLI client instead.
Use nginx as a reverse proxy with HTTPS. Obtain an SSL certificate (e.g., from Let’s Encrypt) and configure nginx to proxy to the local MCP server on port 8000.
Four tools: `get_ec2_spend_last_day`, `get_detailed_breakdown_by_day`, `get_bedrock_daily_usage_stats`, and `get_bedrock_hourly_usage_stats`. Each returns spending or usage data in a readable format.
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