What data sources does this server use?
It reads data from AWS Cost Explorer API and Amazon Bedrock model invocation logs stored in Amazon CloudWatch Logs.
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基本信息
MCP server for understanding AWS spend
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 natural-language access to AWS Cost Explorer data and Amazon Bedrock model invocation logs stored in Amazon CloudWatch. It connects Claude Desktop or any MCP‑enabled client to AWS billing and usage APIs, enabling conversational analysis of spend and model usage.
Install Python 3.12 and uv, clone the repository, create a virtual environment, install dependencies, and configure AWS credentials. Run server.py with environment variables MCP_TRANSPORT (set to stdio for local or sse for remote), BEDROCK_LOG_GROUP_NAME, and optionally CROSS_ACCOUNT_ROLE_NAME. For local use, configure Claude Desktop via Docker or UV arguments. For remote use, deploy the server on EC2 and connect via SSE transport with a CLI client or Chainlit app.
It reads data from AWS Cost Explorer API and Amazon Bedrock model invocation logs stored in Amazon CloudWatch Logs.
Python 3.12, `uv`, and AWS credentials with read‑only access to Cost Explorer and CloudWatch Logs. Optionally, Amazon Bedrock for the LangGraph agent and EC2 for remote hosting.
Yes. Set the `CROSS_ACCOUNT_ROLE_NAME` environment variable, then provide the target AWS account ID in your query. The server assumes the role in that account.
Yes. See the “secure remote MCP server” section in the README for HTTPS setup. Note that MCP itself does not include authentication; do not transmit sensitive data over plain MCP.
Local mode uses `stdio` (stdin/stdout). Remote mode uses `sse` (server‑sent events) over TCP port 8000. Claude Desktop currently only supports `stdio`.
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An MCP server for Azure DevOps

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Open source MCP Servers for AWS
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