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Cloud InfrastructureAuto-generatedScore: 46

AWS Service Catalog MCP Server

AWS Service Catalog is a managed service provided by Amazon Web Services (AWS) that enables organizations to create, manage, and govern catalogs of approved IT services, such as virtual machines, databases, storage buckets, and complex multi-tier applications.

Quick Start Summary

The AWS Service Catalog MCP server is a Model Context Protocol bridge that connects AI assistants — including Claude Desktop, Cursor, Windsurf, and VS Code Copilot — to the AWS Service Catalog API through natural language. It exposes 10 API endpoints as callable tools, such as AcceptPortfolioShare, AssociateBudgetWithResource, AssociatePrincipalWithPortfolio, 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-servicecatalog. This integration is sourced from the auto AWS Service Catalog OpenAPI specification (v2015-12-10) 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
v2015-12-10
Install Command
npx -y @mcp/amazonaws-com-servicecatalog

Environment Variables

AWS_SERVICE_CATALOG_API_KEY

Example: your_aws_service_catalog_api_key

Top Endpoints

POST
/#X-Amz-Target=AWS242ServiceCatalogService.AcceptPortfolioShare

AcceptPortfolioShare

POST
/#X-Amz-Target=AWS242ServiceCatalogService.AssociateBudgetWithResource

AssociateBudgetWithResource

POST
/#X-Amz-Target=AWS242ServiceCatalogService.AssociatePrincipalWithPortfolio

AssociatePrincipalWithPortfolio

POST
/#X-Amz-Target=AWS242ServiceCatalogService.AssociateProductWithPortfolio

AssociateProductWithPortfolio

POST
/#X-Amz-Target=AWS242ServiceCatalogService.AssociateServiceActionWithProvisioningArtifact

AssociateServiceActionWithProvisioningArtifact

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📖 Detailed MCP Integration Guide

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

Capabilities & Use Cases
AWS Service Catalog is a managed service provided by Amazon Web Services (AWS) that enables organizations to create, manage, and govern catalogs of approved IT services, such as virtual machines, databases, storage buckets, and complex multi-tier applications. At its core, the service provides a centralized framework for enterprise teams, including cloud administrators and platform engineers, to curate standardized, secure, and compliant cloud resources for consumption by developers, data scientists, and business units. This ensures that the cloud resources provisioned within an organization adhere to best practices for security, cost control, and operational efficiency, effectively balancing the need for speed with the imperative of governance. Typical use cases include enforcing standardized AMI versions for EC2 instances, managing a menu of pre-configured Amazon RDS database templates with specific parameter groups, and deploying entire application stacks via AWS CloudFormation templates while controlling access and tracking usage.
🤖AI Agent Value
When the AWS Service Catalog API is exposed as a set of tools via the Model Context Protocol (MCP) to an AI coding assistant like Claude Desktop, Cursor, or Cline, it transforms the AI from a code generator into a dynamic cloud operations partner. The AI agent gains real-time, programmatic awareness of and control over the organization's approved service catalog. This integration is particularly powerful for accelerating development workflows and automating governance. A developer can instruct the AI to discover available products and their versions before writing infrastructure-as-code, ensuring they reference valid artifacts. The AI can automate the complex setup of a new portfolio, including associating the correct launch constraints and access controls, by translating a high-level request into a precise sequence of API calls. It can also facilitate continuous compliance by querying constraints and associations to verify a product configuration against organizational policies before deployment.
💬Example Workflows
Practical workflow examples demonstrate significant productivity gains. A developer could instruct the AI agent with a command like, "Help me deploy a new, secure S3 bucket for our project. First, check the service catalog for our company-approved S3 bucket product and list its available versions." The AI would use the SearchProducts and DescribeProduct actions to retrieve this information. Subsequently, a command such as "Create a new portfolio named 'Data Science Team Services' and give the IAM role 'DataScienceLead' full access to it" would trigger the AI to orchestrate calls to CreatePortfolio and AssociatePrincipalWithPortfolio. For managing updates, a user could ask, "The 'Windows Server 2022' product in the 'Infrastructure Base' portfolio has a new provisioning artifact. Update the launch constraint to use the latest IAM role." The AI would then sequentially execute UpdateProvisioningArtifact and UpdateConstraint to implement the change safely.
🛡️Security & Auth
Crucially, while the API endpoint list itself does not embed authentication, all requests to the AWS Service Catalog API must be authenticated and authorized using AWS Identity and Access Management (IAM) credentials. Developers integrating this API via an MCP server must adhere to the principle of least privilege. This means creating a dedicated IAM role or user with permissions scoped only to the specific Service Catalog actions required for the AI agent's tasks. For example, an AI agent used for portfolio setup should not have permissions for TerminateProvisionedProduct. The MCP server's connection to AWS must use secure credential management, typically leveraging environment variables or a secured secret store to provide the necessary access key and secret key to the SDK calls. All API traffic should be conducted over HTTPS (which is the default for AWS service endpoints), and organizations should enable AWS CloudTrail logging to audit all actions performed via the API, creating a traceable record of AI-driven operations.

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