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Developer ToolsAuto-generatedScore: 46

AmazonApiGatewayV2 MCP Server

Amazon API Gateway V2 is a fully managed service provided by Amazon Web Services (AWS) that enables developers to create, publish, maintain, monitor, and secure APIs at any scale.

Quick Start Summary

The AmazonApiGatewayV2 MCP server is a Model Context Protocol bridge that connects AI assistants — including Claude Desktop, Cursor, Windsurf, and VS Code Copilot — to the AmazonApiGatewayV2 API through natural language. It exposes 10 API endpoints as callable tools, such as GetApis, CreateApi, ImportApi, and more. No authentication is required — setup takes approximately 30 seconds. The server uses STDIO transport and can be installed by running npx -y @mcp/amazonaws-com-apigatewayv2. This integration is sourced from the auto AmazonApiGatewayV2 OpenAPI specification (v2018-11-29) and has a quality score of 46/99 (fair documentation coverage).

10Endpointstools mapped
NoneAuthopen access
46/99Qualityfair
~30 secSetupno auth
View Hosted Config URLClaude Desktop Setup Guide

Server Details

Category
Developer Tools
Authentication
None
Endpoints
10 operations
Transport
STDIO
Spec Version
v2018-11-29
Install Command
npx -y @mcp/amazonaws-com-apigatewayv2

Environment Variables

AMAZONAPIGATEWAYV2_API_KEY

Example: your_amazonapigatewayv2_api_key

Top Endpoints

GET
/v2/apis

GetApis

POST
/v2/apis

CreateApi

PUT
/v2/apis

ImportApi

GET
/v2/domainnames/{domainName}/apimappings

GetApiMappings

POST
/v2/domainnames/{domainName}/apimappings

CreateApiMapping

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📖 Detailed MCP Integration Guide

A technical breakdown of capabilities, agent workflows, and security/configuration best practices.

⚡Capabilities & Use Cases
Amazon API Gateway V2 is a fully managed service provided by Amazon Web Services (AWS) that enables developers to create, publish, maintain, monitor, and secure APIs at any scale. It serves as the front door for applications to access data, business logic, or functionality from your backend services. This version specifically focuses on building HTTP APIs and WebSocket APIs, offering a more lightweight, cost-effective, and faster alternative to the original REST APIs. Core capabilities include routing requests to AWS Lambda functions or any HTTP endpoint, handling authentication and authorization through built-in mechanisms or custom authorizers, managing custom domain names for a professional endpoint, and deploying API changes through stages (e.g., dev, staging, production). Its primary enterprise use cases involve constructing robust serverless backends for web and mobile applications, implementing microservices architectures with fine-grained service-to-service communication, and creating WebSocket APIs for real-time, bidirectional communication features like chat, live dashboards, or collaborative tools.
🤖AI Agent Value
When this API is exposed as a set of tools via the Model Context Protocol (MCP) to an AI coding assistant, it transforms from a static endpoint into a dynamic, queryable, and automatable resource within the developer's intelligent workflow. The AI agent can directly interact with the API Gateway management plane, turning natural language commands into precise API operations. This integration provides immense value by abstracting away the need to manually navigate the AWS console or memorize complex CLI commands for routine tasks. For instance, instead of a developer searching through the AWS UI to find a specific API ID or deployment status, they can ask the AI agent to list all deployed APIs and their endpoints. The AI can then use the returned data to inform subsequent actions, such as suggesting a new endpoint for a microservice it is helping to architect, thereby creating a seamless bridge between conceptual planning and concrete infrastructure management.
💬Example Workflows
This integration unlocks powerful dynamic workflows where the AI agent acts as an expert infrastructure assistant. Developers can instruct the AI to perform complex, multi-step tasks through simple conversational prompts. For example: "List all of my API Gateway V2 HTTP APIs and identify which ones do not have a production deployment stage." The AI would use the GET /v2/apis tool to gather the list and then logically deduce the missing deployment. Another example: "Create a new authorizer named 'LambdaAuth' for API 'api-123' that uses the Lambda function 'auth-handler'." The AI would construct the necessary payload and call POST /v2/apis/{apiId}/authorizers. It can also be instructed to "Show me the domain mappings for 'api.example.com'" or "Generate a deployment for API 'api-456' and associate it with the 'prod' stage," automating the deployment lifecycle. These capabilities allow the AI to audit configurations, propagate changes consistently, and help maintain governance across a fleet of APIs.
🛡️Security & Auth
Crucially, while the API itself does not enforce authentication on the management endpoints when called via this MCP tool, security is paramount. The authentication should be rigorously enforced at the transport and identity layer. The MCP server implementation must be configured to use secure, time-limited credentials (like AWS IAM roles or access keys with limited permissions) to sign all requests to the AWS API. Developers must adhere to the principle of least privilege, granting the AI agent's identity only the specific permissions it needs (e.g., apigateway:GET, apigateway:POST on specific resources) and never administrative privileges. It is a best practice to use temporary credentials from AWS STS, employ short-lived tokens, and enable detailed API logging and CloudTrail to audit all actions performed by the AI agent. The server should be configured within a secure environment, avoiding exposure of sensitive credentials in logs or client-side code, ensuring that the automation power is balanced with a robust security posture.

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