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Cloud InfrastructureAuto-generatedScore: 46

Amazon DevOps Guru MCP Server

Amazon DevOps Guru is a fully managed artificial intelligence service provided by Amazon Web Services (AWS) designed to enhance operational resilience by automatically detecting anomalous behavior, identifying potential issues, and providing actionable recommendations for applications running on AWS.

Quick Start Summary

The Amazon DevOps Guru MCP server is a Model Context Protocol bridge that connects AI assistants — including Claude Desktop, Cursor, Windsurf, and VS Code Copilot — to the Amazon DevOps Guru API through natural language. It exposes 10 API endpoints as callable tools, such as ListNotificationChannels, AddNotificationChannel, DescribeInsight, 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-devops-guru. This integration is sourced from the auto Amazon DevOps Guru OpenAPI specification (v2020-12-01) 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
v2020-12-01
Install Command
npx -y @mcp/amazonaws-com-devops-guru

Environment Variables

AMAZON_DEVOPS_GURU_API_KEY

Example: your_amazon_devops_guru_api_key

Top Endpoints

POST
/channels

ListNotificationChannels

PUT
/channels

AddNotificationChannel

GET
/insights/{Id}

DescribeInsight

DELETE
/insights/{Id}

DeleteInsight

GET
/accounts/health

DescribeAccountHealth

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📖 Detailed MCP Integration Guide

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

⚡Capabilities & Use Cases
Amazon DevOps Guru is a fully managed artificial intelligence service provided by Amazon Web Services (AWS) designed to enhance operational resilience by automatically detecting anomalous behavior, identifying potential issues, and providing actionable recommendations for applications running on AWS. The service continuously analyzes operational data, such as Amazon CloudWatch metrics, AWS CloudTrail events, and resource configurations, to identify a wide range of operational risks and inefficiencies, including imminent service disruptions, performance degradations, and cost optimization opportunities. Its core capabilities include proactive insights into resource utilization, root cause analysis for anomalies, and guidance for remediation steps, which are delivered via the service's dashboard and API. Typical enterprise use cases include improving mean time to resolution (MTTR) during incidents, automating operational best practices, and ensuring high availability for business-critical applications like e-commerce platforms, real-time data processing systems, and microservices architectures. By surfacing insights without requiring manual rule-setting or expert analysis, DevOps Guru empowers development, operations, and site reliability engineering (SRE) teams to shift from reactive to proactive operational management.
🤖AI Agent Value
When Amazon DevOps Guru is exposed as a set of tools to an AI coding assistant via the Model Context Protocol (MCP), its value is fundamentally transformed from a dashboard-centric analysis tool into a dynamic, queryable knowledge base and action engine. An AI assistant like Claude, Cursor, or Cline gains the ability to programmatically interact with the operational intelligence layer of an AWS environment. Instead of manually navigating the DevOps Guru console or writing custom scripts to fetch data, a developer can instruct the AI to perform complex queries and automations in natural language. The MCP server exposes key API endpoints for retrieving insights, anomalies, and health summaries, as well as for managing notification channels and feedback loops. This integration allows the AI to act as an intelligent operations partner that can understand the current operational state, explain why an alert was triggered, suggest corrective code or configuration changes, and even initiate the configuration of alerting channels—all within the same development workflow.
💬Example Workflows
Practical workflow examples demonstrate significant productivity and operational gains. A developer encountering a performance degradation could instruct the AI agent: "Query DevOps Guru for the latest critical insights related to my api-gateway-prod resource group and summarize the root cause and recommended actions." The AI would use the GET /insights/{Id} endpoint to fetch relevant details and present a concise analysis. To automate incident response, a user could direct the AI: "Set up a new Microsoft Teams notification channel for all high-severity anomalies in the payment-service application and associate it with this SNS topic ARN," prompting the AI to orchestrate calls to POST /channels and PUT /channels. For proactive maintenance, the command "Generate a report of all unresolved anomalies detected in the last 24 hours across our ECS clusters and draft a summary ticket for our backlog" would have the AI agent leverage GET /anomalies/{Id} and POST /accounts/overview to compile the data. Furthermore, the AI can manage the feedback loop by ingesting developer comments to improve future insights via POST /feedback, or by querying the health of monitored accounts with GET /accounts/health to ensure coverage is complete.
🛡️Security & Auth
While the API itself lists "None" for authentication, this refers to the lack of authentication required for calling the MCP tool endpoints locally. The underlying Amazon DevOps Guru service and its API are secured via AWS Identity and Access Management (IAM) and require valid AWS credentials with appropriate permissions. Developers setting up this MCP server must adhere to the principle of least privilege, creating a dedicated IAM user or role with only the specific devops-guru:* permissions required for the intended operations (e.g., devops-guru:ListInsights, devops-guru:DescribeAccountHealth). Best practices include never hardcoding AWS credentials, using environment variables or secure credential stores, and configuring the MCP server in a secure environment with network controls. It is crucial to understand that the MCP server acts as a bridge; all actual data access and mutations are executed with the permissions of the configured AWS credentials, making robust IAM policies essential to protect sensitive operational data and prevent unintended changes to monitoring configurations.

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