aws-api

Official
Local
8.1k
GitHub Repo

Overview

The aws-api MCP server is an MCP (Model Context Protocol) server that enables AI assistants and agents to interact directly and securely with AWS services and APIs through a standardized, protocol-driven interface. It acts as a bridge between your MCP-compatible AI workflows and the breadth of AWS’s cloud API surface, allowing natural-language-orchestrated tasks — from exploring resources to managing infrastructure — without manual API coding.strate your graph operations without bespoke tooling.

Transport

stdio

Tools

  • call_aws
  • suggest_aws_commands

Key Capabilities

  • Programmatic AWS API access — Execute, describe, and manage AWS resources using natural-language prompts translated into actual AWS API calls.
  • Multi-service interaction — Work across the extensive AWS ecosystem — compute, storage, networking, security, and more — without writing bespoke code for each integration.
  • Secure authentication — Use scoped AWS Identity and Access Management (IAM) credentials to ensure agents can only perform authorized actions.
  • Discovery and exploration — Enumerate available AWS APIs and understand what capabilities are supported in your environment or region.
  • Infrastructure automation — Perform real-time AWS tasks such as creating, updating, or deleting cloud resources using natural language workflows embedded in AI assistants.

How It Works

The aws-api-mcp-server runs as a local or remote process that listens for MCP calls from an AI client (such as Claude Desktop, Cursor, or any MCP-compatible tool). When the agent invokes a tool like execute_api_call, the server translates the MCP request into an AWS API call signed with your AWS credentials (IAM access key, secret key, optionally session tokens). The server handles parameter validation, authentication, and response formatting to conform with the MCP protocol.

Because the AWS API MCP server understands AWS’s expansive API surface, agents can dynamically explore and interact with services without manually maintaining integration code. Internally, the server also logs operations to help with debugging and auditability — logs can be routed to local files or centralized logging services like Amazon CloudWatch for operational insight.

Scopes and permissions are controlled via IAM, so only actions permitted under your provided credentials are executed. This ensures your AI agent respect AWS security boundaries while performing tasks such as provisioning compute resources, querying resource states, or updating configuration.