Skip to content
MCP-Bridge
Cloud InfrastructureAuto-generatedScore: 46

AmazonMQ MCP Server

Amazon MQ is a fully managed message broker service provided by Amazon Web Services (AWS) that enables developers to migrate from self-managed message brokers to the cloud without rewriting applications or reinventing messaging infrastructure.

Quick Start Summary

The AmazonMQ MCP server is a Model Context Protocol bridge that connects AI assistants — including Claude Desktop, Cursor, Windsurf, and VS Code Copilot — to the AmazonMQ API through natural language. It exposes 10 API endpoints as callable tools, such as ListBrokers, CreateBroker, ListConfigurations, 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-mq. This integration is sourced from the auto AmazonMQ OpenAPI specification (v2017-11-27) 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
Cloud Infrastructure
Authentication
None
Endpoints
10 operations
Transport
STDIO
Spec Version
v2017-11-27
Install Command
npx -y @mcp/amazonaws-com-mq

Environment Variables

AMAZONMQ_API_KEY

Example: your_amazonmq_api_key

Top Endpoints

GET
/v1/brokers

ListBrokers

POST
/v1/brokers

CreateBroker

GET
/v1/configurations

ListConfigurations

POST
/v1/configurations

CreateConfiguration

GET
/v1/tags/{resource-arn}

ListTags

Own this API?

Verify ownership of this listing to control the description, configuration details, and documentation links. Choose between free manual verification or instant premium placement.

Option 1: Free Verification

Slow manual review. Requires creating a GitHub issue with verified documentation or domain verification.

  • • Verified badge on page
  • • Standard search sorting
  • • 2-3 business days review
Start Free Claim →
Instant & Boosted

Option 2: Featured Upgrade($9/mo)

Instant verification plus premium styling, featured badges, and directory placement boost.

  • • ★ Featured star & amber highlight border
  • • Top of directory search placement
  • • Instant activation via claim token

📖 Detailed MCP Integration Guide

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

⚡Capabilities & Use Cases
Amazon MQ is a fully managed message broker service provided by Amazon Web Services (AWS) that enables developers to migrate from self-managed message brokers to the cloud without rewriting applications or reinventing messaging infrastructure. The AmazonMQ API exposes a comprehensive set of operations for provisioning, configuring, and managing message broker instances running Apache ActiveMQ and RabbitMQ engines. Through endpoints like GET /v1/brokers and POST /v1/brokers, developers can programmatically list existing broker deployments or create new broker instances with specific engine types, instance sizes, deployment modes (single-instance or high-availability), and network configurations. The configuration management endpoints, GET /v1/configurations and POST /v1/configurations, allow teams to define and retrieve broker-level configuration templates that can be applied across multiple instances for consistency. User lifecycle management is handled through a dedicated set of endpoints targeting individual broker users by broker ID and username, enabling creation (POST), inspection (GET), modification (PUT), and deletion (DELETE) of user accounts with granular permission sets. The tag management endpoints, GET /v1/tags/{resource-arn} and POST /v1/tags/{resource-arn}, provide the ability to attach, update, and query metadata tags on any broker resource using its Amazon Resource Name, which is essential for cost allocation, access control, and organizational governance in large-scale enterprise environments. Typical use cases span microservices decoupling, event-driven architectures, order processing pipelines, IoT telemetry ingestion, and legacy application modernization where reliable asynchronous communication is paramount.
🤖AI Agent Value
When exposed as tools to an AI coding assistant through the Model Context Protocol (MCP), the AmazonMQ API gains extraordinary utility as a context-aware, cloud-infrastructure interface that transforms how developers interact with their messaging backbone. An AI agent connected to this MCP server can serve as a real-time operations companion that understands the current state of a developer's broker fleet and can perform infrastructure changes on their behalf through natural language instructions. Rather than requiring developers to manually navigate the AWS Management Console, consult documentation for CLI syntax, or write boilerplate infrastructure-as-code templates, the AI assistant can directly invoke broker creation, user provisioning, and configuration retrieval endpoints based on conversational prompts. The MCP integration effectively turns the AI into an AWS-savvy platform engineer that can cross-reference broker metadata tags with deployment requirements, audit user permissions against security policies, and suggest or execute optimizations. This is particularly valuable during rapid prototyping, incident response, or onboarding scenarios where developers need immediate access to broker state without context switching between their editor and cloud dashboards. The structured nature of the API responses also means the AI can parse, summarize, and reason over broker configurations to provide actionable recommendations such as identifying underutilized instances or flagging security misconfigurations.
💬Example Workflows
In practical workflows, a developer could instruct an AI agent to perform a wide range of dynamic tasks leveraging the AmazonMQ MCP server. For instance, a developer might say "Show me all the ActiveMQ brokers in our staging environment and their current users," prompting the AI to first invoke GET /v1/brokers to retrieve the broker inventory, filter by engine type and environment tags queried via GET /v1/tags/{resource-arn}, and then iterate through each broker calling GET /v1/brokers/{broker-id}/users/{username} to compile a comprehensive user audit report. Another scenario might involve the developer requesting "Create a new RabbitMQ broker for our payment service with three users having distinct permission levels," where the AI would chain POST /v1/brokers with specific engine and sizing parameters, followed by multiple POST /v1/brokers/{broker-id}/users/{username} calls to establish admin, producer, and consumer user roles. During refactoring or security hardening, a developer could instruct the AI to "Remove the legacy test user from all production brokers and tag those brokers as audited," which would involve the AI listing production brokers, iterating through user deletion via DELETE /v1/brokers/{broker-id}/users/{username}, and applying audit tags through POST /v1/tags/{resource-arn}. The AI can also facilitate configuration drift detection by fetching configurations via GET /v1/configurations and comparing them against a declared baseline, or assist in disaster recovery planning by extracting broker specifications to generate equivalent infrastructure-as-code templates.
🛡️Security & Auth
Developers integrating the AmazonMQ API through MCP should be acutely aware of the authentication posture and security implications. The specification indicates that authentication is set to None, meaning the MCP server does not enforce AWS IAM credential validation, API key checks, or OAuth token verification on incoming requests. This configuration is suitable only for local development, isolated sandbox environments, or scenarios where the MCP transport layer itself provides mutual TLS or network-level access control. In any production or shared environment, developers must layer additional security controls such as running the MCP server behind a reverse proxy with strict IP allowlisting, implementing an authentication gateway that injects AWS Signature Version 4 credentials before forwarding requests to AWS, or using environment-scoped IAM roles with the principle of least privilege granting only the specific AmazonMQ actions required. It is critical that broker user management endpoints not be exposed without proper authorization, as unauthorized user creation or privilege escalation on a message broker can compromise the integrity of entire event-driven systems. Developers should also enable AWS CloudTrail logging for all AmazonMQ API calls to maintain an audit trail, restrict tag-based resource access through AWS Organizations service control policies, and regularly rotate broker credentials. When deploying the MCP server alongside AI assistants, ensure that conversation logs do not inadvertently capture sensitive broker endpoints, usernames, or configuration details, and consider implementing a review step before destructive operations like user deletion or broker teardown are executed against live infrastructure.

Similar APIs

Other APIs in the Cloud Infrastructure category.

Supabase API

Manage Supabase projects, databases, authentication, and storage through your AI agent.

API Key

Cloudflare API

Manage Cloudflare DNS, CDN, Workers, and security settings through your AI agent.

API Key

Vercel API

Deploy projects, manage domains, and monitor deployments through your AI agent.

Bearer Token

DigitalOcean API

The DigitalOcean API is a comprehensive, RESTful interface provided by DigitalOcean, a leading cloud infrastructure provider focused on simplifying cloud computing for developers, startups, and enterprises. It serves as the programmatic backbone for managing the entire DigitalOcean ecosystem, enabling users to provision, configure, and control cloud resources such as Droplets (virtual private servers), Kubernetes clusters, managed databases, networks, storage volumes, and application platforms. Core capabilities include full lifecycle management of these resources, from creation and scaling to monitoring and deletion, mirroring the functionality available in the DigitalOcean control panel. Its primary use cases range from automating infrastructure setup for CI/CD pipelines and enabling infrastructure-as-code practices to supporting dynamic application scaling and resource optimization for SaaS products, e-commerce sites, and development environments. The API is designed for both developers seeking to automate their cloud operations and businesses that require programmable, scalable cloud infrastructure without the complexity of larger hyperscale providers.

Related MCP Server Integrations

Supabase API MCP Setup

Manage Supabase projects, databases, authentication, and storage through your AI agent.

Cloud InfrastructureConfigure →

Cloudflare API MCP Setup

Manage Cloudflare DNS, CDN, Workers, and security settings through your AI agent.

Cloud InfrastructureConfigure →

Vercel API MCP Setup

Deploy projects, manage domains, and monitor deployments through your AI agent.

Cloud InfrastructureConfigure →

DigitalOcean API MCP Setup

The DigitalOcean API is a comprehensive, RESTful interface provided by DigitalOcean, a leading cloud infrastructure provider focused on simplifying cloud computing for developers, startups, and enterprises. It serves as the programmatic backbone for managing the entire DigitalOcean ecosystem, enabling users to provision, configure, and control cloud resources such as Droplets (virtual private servers), Kubernetes clusters, managed databases, networks, storage volumes, and application platforms. Core capabilities include full lifecycle management of these resources, from creation and scaling to monitoring and deletion, mirroring the functionality available in the DigitalOcean control panel. Its primary use cases range from automating infrastructure setup for CI/CD pipelines and enabling infrastructure-as-code practices to supporting dynamic application scaling and resource optimization for SaaS products, e-commerce sites, and development environments. The API is designed for both developers seeking to automate their cloud operations and businesses that require programmable, scalable cloud infrastructure without the complexity of larger hyperscale providers.

Cloud InfrastructureConfigure →

Access Analyzer MCP Setup

The AWS Identity and Access Management Access Analyzer API provides a powerful, policy-as-code service that automatically identifies resources accessible from outside your AWS account or organization. At its core, the service continuously evaluates resource-based policies—such as Amazon S3 bucket policies, AWS Identity and Access Management (IAM) roles, Amazon KMS key policies, and AWS Lambda function policies—using logic-based reasoning to determine which resources grant access to unknown external principals. Its primary use case is for security and compliance teams within enterprises to proactively detect unintended data exposure, enforce least privilege principles, and audit cross-account and cross-service access. The API endpoints allow programmatic control to create, configure, and query analyzers, manage archive rules for storing findings, and generate custom policy documents, making it a foundational tool for automating cloud security posture management at scale.

Cloud InfrastructureConfigure →