Skip to content
AI & MLAuto-generatedScore: 46

Amazon Machine Learning MCP Server

The Amazon Machine Learning API is a comprehensive, programmable interface provided by Amazon Web Services (AWS) that enables developers and data scientists to build, train, and deploy predictive models at scale.

Quick Start Summary

The Amazon Machine Learning MCP server is a Model Context Protocol bridge that connects AI assistants — including Claude Desktop, Cursor, Windsurf, and VS Code Copilot — to the Amazon Machine Learning API through natural language. It exposes 10 API endpoints as callable tools, such as AddTags, CreateBatchPrediction, CreateDataSourceFromRDS, 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-machinelearning. This integration is sourced from the auto Amazon Machine Learning OpenAPI specification (v2014-12-12) and has a quality score of 46/99 (fair documentation coverage).

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

Server Details

Category
AI & ML
Authentication
None
Endpoints
10 operations
Transport
STDIO
Spec Version
v2014-12-12
Install Command
npx -y @mcp/amazonaws-com-machinelearning

Environment Variables

AMAZON_MACHINE_LEARNING_API_KEY

Example: your_amazon_machine_learning_api_key

Top Endpoints

POST
/#X-Amz-Target=AmazonML_20141212.AddTags

AddTags

POST
/#X-Amz-Target=AmazonML_20141212.CreateBatchPrediction

CreateBatchPrediction

POST
/#X-Amz-Target=AmazonML_20141212.CreateDataSourceFromRDS

CreateDataSourceFromRDS

POST
/#X-Amz-Target=AmazonML_20141212.CreateDataSourceFromRedshift

CreateDataSourceFromRedshift

POST
/#X-Amz-Target=AmazonML_20141212.CreateDataSourceFromS3

CreateDataSourceFromS3

Own this API?

Verify ownership of this listing to control the description, configuration details, and documentation links. Choose between free manual verification or instant premium placement.

Option 1: Free Verification

Slow manual review. Requires creating a GitHub issue with verified documentation or domain verification.

  • • Verified badge on page
  • • Standard search sorting
  • • 2-3 business days review
Start Free Claim →
Instant & Boosted

Option 2: Featured Upgrade($9/mo)

Instant verification plus premium styling, featured badges, and directory placement boost.

  • • ★ Featured star & amber highlight border
  • • Top of directory search placement
  • • Instant activation via claim token

📖 Detailed MCP Integration Guide

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

Capabilities & Use Cases
The Amazon Machine Learning API is a comprehensive, programmable interface provided by Amazon Web Services (AWS) that enables developers and data scientists to build, train, and deploy predictive models at scale. At its core, it abstracts the complexity of the entire machine learning pipeline, offering managed services for data ingestion from sources like Amazon S3, Amazon Redshift, or relational databases; exploratory data analysis; model building using algorithms for regression, classification, and forecasting; rigorous evaluation; and operationalization via batch prediction or real-time endpoints. This API is fundamental for enterprise applications requiring predictive intelligence, such as demand forecasting for supply chain optimization, customer churn prediction for retention campaigns, fraud detection in financial transactions, and personalized recommendation engines for e-commerce platforms. By exposing a declarative, state-driven model where resources like data sources, ML models, and endpoints are discrete entities, it provides a structured and auditable framework for operationalizing machine learning within larger application architectures.
🤖AI Agent Value
When this API is exposed as a set of tools to an AI coding assistant via the Model Context Protocol (MCP), it transforms the assistant into a powerful ML operations copilot. The MCP server acts as a secure bridge, allowing the AI to understand and interact with the ML lifecycle through natural language commands. The value is profound: it accelerates development by automating boilerplate code for SDK calls, reduces context-switching between documentation and code editors, and lowers the barrier to entry for developers not deeply versed in ML specifics. An assistant can serve as an interactive guide, explaining the implications of different model types, suggesting parameter optimizations based on the described use case, and providing real-time feedback on resource configuration. This integration moves beyond code completion to intelligent orchestration, where the AI can reason about the end-to-end workflow—from data preparation to endpoint deployment—based on high-level user objectives.
💬Example Workflows
In a practical workflow, a developer could instruct the AI agent to perform dynamic, multi-step tasks. For example, a command like "Create a new ML model to predict customer lifetime value using our historical sales data in the 'analytics' S3 bucket, then create an evaluation against the held-out test set" would trigger the agent to sequentially invoke CreateDataSourceFromS3, followed by CreateMLModel, and finally CreateEvaluation, passing the appropriate parameters and resource identifiers. Similarly, a request to "Set up a real-time prediction endpoint for our fraud detection model and tag it for the 'production-finance' team" would result in the agent calling CreateRealtimeEndpoint and then AddTags to organize and manage the resource. Another powerful example is instruction to "Run a batch prediction for the next quarter's demand on all SKUs using model ID 'ml-abc123' and save the results to S3," which would automate the CreateBatchPrediction call with the correct input data location and model reference. This allows developers to focus on the 'what' and 'why' of their ML tasks while the AI agent handles the precise 'how' of the API interactions.
🛡️Security & Auth
Crucial to the security and governance of this integration are robust authentication and access control practices. Although the listed endpoints appear without authentication in the description, interacting with the actual AWS service requires rigorous use of AWS Identity and Access Management (IAM). The MCP server configuration must securely handle AWS credentials, preferably using an IAM role with tightly scoped permissions following the principle of least privilege. For instance, an IAM policy should grant only the specific API actions needed (e.g., machinelearning:CreateMLModel on a designated S3 bucket) and deny all others. All data in transit between the AI assistant, the MCP server, and AWS endpoints must be encrypted via HTTPS/TLS. Furthermore, developers should enable AWS CloudTrail for auditing all API calls made by the service, implement resource tagging consistently for cost allocation and access control, and avoid hardcoding credentials by using environment variables or secure secret management services. Regular review of IAM policies and endpoint access logs is essential to maintain a secure and compliant machine learning operations environment.

Similar APIs

Other APIs in the AI & ML category.

OpenAI API

Generate text, images, and embeddings. Integrate GPT models and DALL-E into your AI agent.

API Key

Anthropic API

Access Claude AI models for text generation, analysis, and code assistance through the Anthropic API.

API Key

OpenAI API

The OpenAI API, developed and maintained by OpenAI, provides programmatic access to a suite of advanced artificial intelligence capabilities centered around large language models (LLMs). Its core functions enable developers to integrate state-of-the-art natural language processing and generation into applications. Key endpoints support text generation (completions, chat completions), content transformation (edits, classifications), semantic analysis (embeddings), and multimodal processing (audio transcriptions and translations). The API serves a broad spectrum of users, from individual developers and startups building conversational agents or content tools to large enterprises automating complex workflows, enhancing customer support, conducting sentiment analysis on large text corpora, or generating synthetic data for training. Use cases span consumer applications like intelligent writing assistants and enterprise-grade solutions for automated document summarization, code generation, and multilingual communication platforms.

Amazon CodeGuru Profiler

Amazon CodeGuru Profiler is an advanced application performance profiling service provided by Amazon Web Services (AWS). It continuously collects runtime performance data—such as CPU utilization, memory allocation, and thread contention—from live production applications, then analyzes this data using machine learning algorithms to pinpoint performance bottlenecks and inefficiencies. The API serves as the programmatic interface for managing the profiling lifecycle, allowing developers to create and configure profiling groups, adjust agent settings, retrieve performance metrics and findings, and manage notification configurations. Enterprise use cases include optimizing microservice latency in high-traffic systems, reducing cloud compute costs by identifying inefficient code paths, and maintaining application health in continuous deployment pipelines where performance regressions must be detected early. For development teams, it provides actionable insights to guide code optimization efforts based on real-world usage rather than synthetic benchmarks.

Related MCP Server Integrations

OpenAI API MCP Setup

Generate text, images, and embeddings. Integrate GPT models and DALL-E into your AI agent.

AI & MLConfigure →

Anthropic API MCP Setup

Access Claude AI models for text generation, analysis, and code assistance through the Anthropic API.

AI & MLConfigure →

OpenAI API MCP Setup

The OpenAI API, developed and maintained by OpenAI, provides programmatic access to a suite of advanced artificial intelligence capabilities centered around large language models (LLMs). Its core functions enable developers to integrate state-of-the-art natural language processing and generation into applications. Key endpoints support text generation (completions, chat completions), content transformation (edits, classifications), semantic analysis (embeddings), and multimodal processing (audio transcriptions and translations). The API serves a broad spectrum of users, from individual developers and startups building conversational agents or content tools to large enterprises automating complex workflows, enhancing customer support, conducting sentiment analysis on large text corpora, or generating synthetic data for training. Use cases span consumer applications like intelligent writing assistants and enterprise-grade solutions for automated document summarization, code generation, and multilingual communication platforms.

AI & MLConfigure →

Amazon CodeGuru Profiler MCP Setup

Amazon CodeGuru Profiler is an advanced application performance profiling service provided by Amazon Web Services (AWS). It continuously collects runtime performance data—such as CPU utilization, memory allocation, and thread contention—from live production applications, then analyzes this data using machine learning algorithms to pinpoint performance bottlenecks and inefficiencies. The API serves as the programmatic interface for managing the profiling lifecycle, allowing developers to create and configure profiling groups, adjust agent settings, retrieve performance metrics and findings, and manage notification configurations. Enterprise use cases include optimizing microservice latency in high-traffic systems, reducing cloud compute costs by identifying inefficient code paths, and maintaining application health in continuous deployment pipelines where performance regressions must be detected early. For development teams, it provides actionable insights to guide code optimization efforts based on real-world usage rather than synthetic benchmarks.

AI & MLConfigure →

Amazon CodeGuru Reviewer MCP Setup

The Amazon CodeGuru Reviewer API is a powerful programmatic interface to Amazon's automated code analysis service, designed to elevate code quality and developer productivity. This API exposes the core functionalities of a managed service that combines deep static analysis, machine learning models trained on vast code repositories, and pattern recognition to identify complex defects, security vulnerabilities, and non-idiomatic code patterns that are often missed in manual reviews. Specifically targeting Java and Python codebases, CodeGuru Reviewer analyzes code changes submitted through integrated repositories like AWS CodeCommit, GitHub, or Bitbucket, and generates actionable recommendations. Its primary enterprise use cases are integrated into continuous integration and continuous delivery (CI/CD) pipelines for automated, mandatory code quality gates; conducting security and compliance audits on critical application code; and providing scalable, consistent feedback during the pull request process, thereby reducing the burden on human reviewers and accelerating safe code deployments.

AI & MLConfigure →