SageMaker AI MCP Server

SageMaker AI MCP Server

Enables AI assistants to manage Amazon SageMaker AI resources including endpoints, jobs, pipelines, MLflow tracking servers, domains, models, model cards, and apps.

Category
访问服务器

README

SageMaker AI MCP Server

A Model Context Protocol (MCP) server for Amazon SageMaker AI that enables AI assistants to access, work and manage SageMaker AI resources.

Features

  • Managing and working with SageMaker AI endpoint resources
  • Managing and working with SageMaker AI training, processing and transform jobs
  • Managing and working with SageMaker AI pipelines
  • CRUD operations for SageMaker AI Managed MLflow Tracking Server
  • CRUD operations for SageMaker AI Domain
  • Managing and working with Models and Model Cards
  • CRUD operatioons for SageMaker AI Recommender Jobs
  • CRUD operations for SageMaker AI Apps

Prerequisites

  1. Install uv from Astral or the GitHub README
  2. Install Python using uv python install 3.10
  3. Set up AWS credentials with access to Amazon SageMaker AI resources.
    • You need an AWS account with Amazon SageMaker AI enabled
    • Configure AWS credentials with aws configure or environment variables
    • Ensure your IAM role/user has permissions to use Amazon SageMaker AI
  4. Create a SageMaker Execution Role with the necessary permissions for SageMaker AI operations

Installation

WIP

Environment Variables

  • AWS_PROFILE: AWS CLI profile to use for credentials
  • AWS_REGION: AWS region to use (default: us-east-1)
  • SAGEMAKER_EXECUTION_ROLE_ARN: ARN of the SageMaker execution role

AWS Authentication

The server uses the AWS profile specified in the AWS_PROFILE environment variable. If not provided, it defaults to the default credential provider chain.

"env": {
  "AWS_PROFILE": "your-aws-profile",
  "AWS_REGION": "us-east-1",
  "SAGEMAKER_EXECUTION_ROLE_ARN": "arn:aws:iam::123456789012:role/SageMakerExecutionRole"
}

Make sure the AWS profile has permissions to access Amazon SageMaker AI services. The MCP server creates a boto3 session using the specified profile to authenticate with AWS services.

Tools

List of Tools for SageMaker AI Endpoints and Endpoint Configurations

  • list_endpoints_sagemaker (List all SageMaker AI Endpoints)
  • list_endpoint_configs_sagemaker (List all SageMaker AI Endpoint Configurations)
  • describe_endpoint_sagemaker (Describe a SageMaker AI Endpoint)
  • describe_endpoint_config_sagemaker (Describe a SageMaker AI Endpoint Configuration)
  • delete_endpoint_sagemaker (Delete a SageMaker AI Endpoint)
  • delete_endpoint_config_sagemaker (Delete a SageMaker AI Endpoint Configuration)

List of Tools for SageMaker AI Jobs

  • list_training_jobs_sagemaker (List all SageMaker AI Training Jobs)
  • list_processing_jobs_sagemaker (List all SageMaker AI Processing Jobs)
  • list_transform_jobs_sagemaker (List all SageMaker AI Transform Jobs)
  • list_inference_recommender_jobs_sagemaker (List all SageMaker AI Inference Recommender Jobs)
  • list_inference_recommender_job_steps_sagemaker (List all steps for a SageMaker AI Inference Recommender Job)
  • describe_training_job_sagemaker (Describe a SageMaker AI Training Job)
  • describe_processing_job_sagemaker (Describe a SageMaker AI Processing Job)
  • describe_transform_job_sagemaker (Describe a SageMaker AI Transform Job)
  • describe_inference_recommender_job_sagemaker (Describe a SageMaker AI Inference Recommender Job)
  • stop_training_job_sagemaker (Stop a SageMaker AI Training Job)
  • stop_processing_job_sagemaker (Stop a SageMaker AI Processing Job)
  • stop_transform_job_sagemaker (Stop a SageMaker AI Transform Job)
  • stop_inference_recommender_job_sagemaker (Stop a SageMaker AI Inference Recommender Job)

List of Tools for SageMaker AI Pipelines

  • list_pipelines_sagemaker (List all SageMaker AI Pipelines)
  • list_pipeline_executions_sagemaker (List all Pipeline Executions for a SageMaker AI Pipeline)
  • list_pipeline_execution_steps_sagemaker (List all steps for a SageMaker AI Pipeline Execution)
  • list_pipeline_parameters_for_execution_sagemaker (List all parameters for a SageMaker AI Pipeline Execution)
  • describe_pipeline_sagemaker (Describe a SageMaker AI Pipeline)
  • describe_pipeline_execution_sagemaker (Describe a SageMaker AI Pipeline Execution)
  • describe_pipeline_definition_for_execution_sagemaker (Describe a SageMaker AI Pipeline Definition for Execution)
  • start_pipeline_execution_sagemaker (Start a SageMaker AI Pipeline Execution)
  • stop_pipeline_execution_sagemaker (Stop a SageMaker AI Pipeline Execution)
  • delete_pipeline_sagemaker (Delete a SageMaker AI Pipeline)

List of Tools for SageMaker AI User Profiles and Spaces

  • list_user_profiles_sagemaker (List all SageMaker AI User Profiles)
  • list_spaces_sagemaker (List all SageMaker AI Spaces)

List of Tools for SageMaker AI MLflow Managed Tracking Servers

  • list_mlflow_tracking_servers_sagemaker (List all MLflow Tracking Servers)
  • create_mlflow_tracking_server_sagemaker (Create a new MLflow Tracking Server)
  • create_presigned_mlflow_tracking_server_url_sagemaker (Create a presigned URL for an MLflow Tracking Server)
  • describe_mlflow_tracking_server_sagemaker (Describe an MLflow Tracking Server)
  • start_mlflow_tracking_server_sagemaker (Start an MLflow Tracking Server)
  • stop_mlflow_tracking_server_sagemaker (Stop an MLflow Tracking Server)
  • delete_mlflow_tracking_server_sagemaker (Delete an MLflow Tracking Server)

List of Tools for SageMaker AI Domains

  • list_domains_sagemaker (List all SageMaker AI Domains)
  • create_presigned_domain_url_sagemaker (Create a presigned URL for a SageMaker Domain)
  • describe_domain_sagemaker (Describe a SageMaker AI Domain)
  • delete_domain_sagemaker (Delete a SageMaker AI Domain)

List of Tools for SageMaker AI Models

  • list_models_sagemaker (List all SageMaker AI Models)
  • describe_model_sagemaker (Describe a SageMaker AI Model)
  • delete_model_sagemaker (Delete a SageMaker AI Model)

List of Tools for SageMaker AI Model Cards

  • list_model_cards_sagemaker (List all SageMaker AI Model Cards)
  • list_model_card_export_jobs_sagemaker (List all SageMaker AI Model Card Export Jobs)
  • list_model_card_versions_sagemaker (List all versions of a SageMaker AI Model Card)
  • describe_model_card_sagemaker (Describe a SageMaker AI Model Card)
  • delete_model_card_sagemaker (Delete a SageMaker AI Model Card)

List of Tools for SageMaker AI Apps

  • list_apps_sagemaker (List all SageMaker AI Apps)
  • create_app_sagemaker (Create a SageMaker AI App)
  • create_presigned_notebook_instance_url_sagemaker (Create a presigned URL for a SageMaker Notebook Instance)
  • describe_app_sagemaker (Describe a SageMaker AI App)
  • describe_app_image_config_sagemaker (Describe a SageMaker AI App Image Config)
  • delete_app_sagemaker (Delete a SageMaker AI App)
  • delete_app_image_config_sagemaker (Delete a SageMaker AI App Image Config)

Security Considerations

  • Use AWS IAM roles with appropriate permissions
  • Store credentials securely
  • Use temporary credentials when possible

License

This project is licensed under the Apache License, Version 2.0. See the LICENSE file for details.

推荐服务器

Baidu Map

Baidu Map

百度地图核心API现已全面兼容MCP协议,是国内首家兼容MCP协议的地图服务商。

官方
精选
JavaScript
Playwright MCP Server

Playwright MCP Server

一个模型上下文协议服务器,它使大型语言模型能够通过结构化的可访问性快照与网页进行交互,而无需视觉模型或屏幕截图。

官方
精选
TypeScript
Magic Component Platform (MCP)

Magic Component Platform (MCP)

一个由人工智能驱动的工具,可以从自然语言描述生成现代化的用户界面组件,并与流行的集成开发环境(IDE)集成,从而简化用户界面开发流程。

官方
精选
本地
TypeScript
Audiense Insights MCP Server

Audiense Insights MCP Server

通过模型上下文协议启用与 Audiense Insights 账户的交互,从而促进营销洞察和受众数据的提取和分析,包括人口统计信息、行为和影响者互动。

官方
精选
本地
TypeScript
VeyraX

VeyraX

一个单一的 MCP 工具,连接你所有喜爱的工具:Gmail、日历以及其他 40 多个工具。

官方
精选
本地
graphlit-mcp-server

graphlit-mcp-server

模型上下文协议 (MCP) 服务器实现了 MCP 客户端与 Graphlit 服务之间的集成。 除了网络爬取之外,还可以将任何内容(从 Slack 到 Gmail 再到播客订阅源)导入到 Graphlit 项目中,然后从 MCP 客户端检索相关内容。

官方
精选
TypeScript
Kagi MCP Server

Kagi MCP Server

一个 MCP 服务器,集成了 Kagi 搜索功能和 Claude AI,使 Claude 能够在回答需要最新信息的问题时执行实时网络搜索。

官方
精选
Python
e2b-mcp-server

e2b-mcp-server

使用 MCP 通过 e2b 运行代码。

官方
精选
Neon MCP Server

Neon MCP Server

用于与 Neon 管理 API 和数据库交互的 MCP 服务器

官方
精选
Exa MCP Server

Exa MCP Server

模型上下文协议(MCP)服务器允许像 Claude 这样的 AI 助手使用 Exa AI 搜索 API 进行网络搜索。这种设置允许 AI 模型以安全和受控的方式获取实时的网络信息。

官方
精选