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Cloud InfrastructureAuto-generatedScore: 46

AWS Systems Manager Incident Manager Contacts MCP Server

AWS Systems Manager Incident Manager Contacts is a critical component of the AWS Incident Manager service, provided by Amazon Web Services (AWS).

Quick Start Summary

The AWS Systems Manager Incident Manager Contacts MCP server is a Model Context Protocol bridge that connects AI assistants — including Claude Desktop, Cursor, Windsurf, and VS Code Copilot — to the AWS Systems Manager Incident Manager Contacts API through natural language. It exposes 10 API endpoints as callable tools, such as AcceptPage, ActivateContactChannel, CreateContact, 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-ssm-contacts. This integration is sourced from the auto AWS Systems Manager Incident Manager Contacts OpenAPI specification (v2021-05-03) 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
v2021-05-03
Install Command
npx -y @mcp/amazonaws-com-ssm-contacts

Environment Variables

AWS_SYSTEMS_MANAGER_INCIDENT_MANAGER_CONTACTS_API_KEY

Example: your_aws_systems_manager_incident_manager_contacts_api_key

Top Endpoints

POST
/#X-Amz-Target=SSMContacts.AcceptPage

AcceptPage

POST
/#X-Amz-Target=SSMContacts.ActivateContactChannel

ActivateContactChannel

POST
/#X-Amz-Target=SSMContacts.CreateContact

CreateContact

POST
/#X-Amz-Target=SSMContacts.CreateContactChannel

CreateContactChannel

POST
/#X-Amz-Target=SSMContacts.CreateRotation

CreateRotation

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📖 Detailed MCP Integration Guide

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

Capabilities & Use Cases
AWS Systems Manager Incident Manager Contacts is a critical component of the AWS Incident Manager service, provided by Amazon Web Services (AWS). It serves as a centralized, programmable contact management and escalation engine designed to automate and streamline incident response workflows. The core capability of this API is to manage the people and communication channels essential for resolving operational incidents affecting AWS-hosted applications. It allows developers and administrators to define complex on-call rotation schedules, create and manage individual contacts (including their multiple communication channels like email, SMS, and voice), and orchestrate automated escalation and acknowledgment procedures. In an enterprise context, this API is indispensable for maintaining business continuity and service level agreements (SLAs). Typical use cases include automatically notifying on-call engineers during a production outage, facilitating rapid team mobilization for critical security incidents, managing scheduled maintenance notifications, and ensuring the right stakeholders are engaged according to predefined, compliance-driven response plans.
🤖AI Agent Value
When integrated as a tool via the Model Context Protocol (MCP) for an AI coding assistant, this API transforms from a manual administration interface into a powerful engine for infrastructure-as-code and automated operational resilience. The value lies in enabling the AI to act as a dynamic incident response architect and manager. An AI assistant with access to these endpoints can directly interpret natural language instructions to build and maintain robust operational runbooks. For instance, it can programmatically create new contact hierarchies, modify on-call rotations in real-time to account for ad-hoc schedule changes, or generate a complete escalation plan based on a team's structure and communication preferences. This shifts the developer's role from performing repetitive, error-prone manual console work to directing high-level operational strategy, with the AI handling the precise, auditable implementation through API calls.
💬Example Workflows
A developer can instruct the AI agent to perform a variety of dynamic, high-value tasks to automate and refine their incident management processes. The AI could be asked to "Query all contacts tagged with the 'database-team' role and activate their primary SMS channel for a high-priority maintenance window," or "Update the on-call rotation for the 'frontend-team' to include a new engineer starting next Monday, and generate a corresponding calendar event." More complex workflows are possible, such as "Analyze the current contact roster, identify any roles without a backup contact, and create new placeholder contacts with deactivation flags to prompt manual filling," or "After a simulated incident drill, use the AcceptPage endpoint to programmatically acknowledge the test alert and close the simulation, verifying the entire escalation chain worked." These actions demonstrate how the AI can bridge planning and execution, ensuring that incident management configurations are always current, consistent, and ready for real-world execution.
🛡️Security & Auth
Proper configuration and adherence to security best practices are paramount when exposing this API through an MCP server. Although the endpoint specification notes "None" for authentication, this refers to the absence of a custom API key within the MCP tool definition itself; all underlying AWS API calls are secured through AWS Identity and Access Management (IAM). The IAM entity (user or role) used by the AI assistant's execution environment must be granted the principle of least privilege. This means attaching only the specific ssmcontacts:* permissions required for the intended tasks, such as ssmcontacts:CreateContactChannel or ssmcontacts:UpdateRotation, and scoping these permissions to relevant resource ARNs where possible. Developers must ensure the MCP server's configuration securely manages AWS credentials, ideally by assuming an IAM role with temporary credentials via the AWS Security Token Service (STS) rather than embedding long-term access keys. Furthermore, all API interactions should be logged via AWS CloudTrail for auditability, allowing teams to trace every automated change made by the AI back to a specific instruction and context, ensuring operational governance and accountability.

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