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Cloud InfrastructureAuto-generatedScore: 46

Amazon Simple Workflow Service MCP Server

Amazon Simple Workflow Service, known as Amazon SWF, is a fully managed cloud orchestration service provided by Amazon Web Services.

Quick Start Summary

The Amazon Simple Workflow Service MCP server is a Model Context Protocol bridge that connects AI assistants — including Claude Desktop, Cursor, Windsurf, and VS Code Copilot — to the Amazon Simple Workflow Service API through natural language. It exposes 10 API endpoints as callable tools, such as CountClosedWorkflowExecutions, CountOpenWorkflowExecutions, CountPendingActivityTasks, 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-swf. This integration is sourced from the auto Amazon Simple Workflow Service OpenAPI specification (v2012-01-25) and has a quality score of 46/99 (fair documentation coverage).

10Endpointstools mapped
NoneAuthopen access
46/99Qualityfair
~30 secSetupno auth

Server Details

Category
Cloud Infrastructure
Authentication
None
Endpoints
10 operations
Transport
STDIO
Spec Version
v2012-01-25
Install Command
npx -y @mcp/amazonaws-com-swf

Environment Variables

AMAZON_SIMPLE_WORKFLOW_SERVICE_API_KEY

Example: your_amazon_simple_workflow_service_api_key

Top Endpoints

POST
/#X-Amz-Target=SimpleWorkflowService.CountClosedWorkflowExecutions

CountClosedWorkflowExecutions

POST
/#X-Amz-Target=SimpleWorkflowService.CountOpenWorkflowExecutions

CountOpenWorkflowExecutions

POST
/#X-Amz-Target=SimpleWorkflowService.CountPendingActivityTasks

CountPendingActivityTasks

POST
/#X-Amz-Target=SimpleWorkflowService.CountPendingDecisionTasks

CountPendingDecisionTasks

POST
/#X-Amz-Target=SimpleWorkflowService.DeprecateActivityType

DeprecateActivityType

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📖 Detailed MCP Integration Guide

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

Capabilities & Use Cases
Amazon Simple Workflow Service, known as Amazon SWF, is a fully managed cloud orchestration service provided by Amazon Web Services. Its core purpose is to simplify the coordination of work across distributed application components, enabling developers to focus on business logic rather than the underlying infrastructure for state management, error handling, and scale. The service models work as sequences of tasks, where a task represents a discrete unit of work executed by a worker. SWF manages the state of these tasks, maintains a history of all events, and guarantees that each task is assigned to exactly one worker and is executed only once. This makes it ideal for building reliable, scalable microservice architectures, processing batch jobs, and creating complex, long-running workflows such as customer order fulfillment, media processing pipelines, or multi-step financial transaction approvals.
🤖AI Agent Value
When exposed as a set of tools via the Model Context Protocol, the SWF API becomes exceptionally valuable for AI coding assistants integrated into development environments like Claude Desktop or Cursor. The MCP server transforms SWF from a static backend service into a dynamic, queryable system that an AI agent can inspect and interact with programmatically. This allows the AI to move beyond code generation and actively assist in the operational understanding, debugging, and management of production workflows. Instead of relying solely on static documentation or manual dashboard checks, a developer can instruct the AI to perform real-time analysis of the workflow state, providing an intelligent bridge between the application code and its live execution environment.
💬Example Workflows
A developer can leverage this integration to instruct the AI to perform a wide range of dynamic operational tasks. For example, a user could ask, "Analyze our video processing workflow domain and list all open workflow executions that have been running for over an hour to identify potential bottlenecks." The AI agent would use the CountOpenWorkflowExecutions and DescribeWorkflowExecution tools to gather this data and provide a summary. Another practical command might be, "Check for any pending decision tasks in the order-processing domain; if there are more than 50, generate a temporary Lambda function to handle the scale." This moves the AI assistant from a passive code advisor to an active participant in system monitoring and automated remediation, capable of querying real-time records to inform decisions, auto-scaling responses, or auditing compliance.
🛡️Security & Auth
Critical configuration for this integration revolves around secure and precise authentication. As specified, this API leverages AWS IAM for authentication, not simple API keys. Therefore, the MCP server configuration must be set up with temporary, scoped AWS credentials. Best practices dictate creating a dedicated IAM role or user for the AI assistant with the principle of least privilege, granting only the precise SWF actions required for its analysis tasks—such as swf:ListWorkflowExecutions, swf:DescribeWorkflowExecution, and swf:CountOpenWorkflowExecutions—and restricting access to specific SWF domains. Credentials should never be hard-coded; instead, use environment variables, AWS secrets managers, or the default credential provider chain of the SDK. Developers must also ensure the MCP server itself is secured, as it now serves as a privileged gateway to workflow coordination data.

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