What are the prerequisites for using AWS FinOps MCP Server?
You need Python 3.10+, the AWS CLI installed and configured, at least one AWS profile with read‑only IAM permissions, and an MCP‑compatible client (e.g., Claude Desktop).
@ravikiranvm
基本信息
An MCP (Model Context Protocol) server that brings powerful AWS FinOps capabilities directly into your AI assistant. Analyze cloud costs, audit for waste, and get budget insights using natural language, all while keeping your credentials secure on your local machine.
使用下面的配置,将此服务器添加到你的 MCP 客户端。
{
"mcpServers": {
"aws-finops-mcp-server": {
"command": "python",
"args": [
"-m",
"pip",
"install",
"--user",
"pipx"
]
}
}
}工具列表暂未提供
当前收录暂时没有可展示的工具列表,不代表服务没有工具。请查阅项目文档了解支持的能力。
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。
An MCP (Model Context Protocol) server that brings AWS FinOps capabilities into your AI assistant. It analyzes cloud costs, audits for waste, and provides budget insights using natural language, while keeping your credentials secure on your local machine.
Install via pipx install aws-finops-mcp-server (or using uv). Configure one or more AWS CLI profiles. Then add the server to your MCP client’s configuration file (e.g., Claude for Desktop’s claude_desktop_config.json). After restarting the client, ask questions about your AWS finances in plain English.
You need Python 3.10+, the AWS CLI installed and configured, at least one AWS profile with read‑only IAM permissions, and an MCP‑compatible client (e.g., Claude Desktop).
Each `get_cost` call makes 2 calls to the AWS Cost Explorer API, costing $0.01. The `run_finops_audit` tool uses other AWS APIs (EC2, Budgets) that are generally in the free tier or have negligible cost.
Claude Desktop and Amazon Q CLI are explicitly supported. Any MCP‑compatible client that supports tools can also be used.
The IAM policy must allow the following read‑only actions: `ce:GetCostAndUsage`, `budgets:ViewBudget`, `ec2:DescribeInstances`, `ec2:DescribeVolumes`, `ec2:DescribeAddresses`, and `sts:GetCallerIdentity`.
The server runs locally and uses your existing AWS CLI configuration. Credentials never leave your machine; API calls are made directly from your local environment to AWS.
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