Documentation
Learn how to leverage MCP to enhance AI systems with accurate, up-to-date ML library documentation.
What is MCP?
Model Context Protocol (MCP) is a specialized platform that provides always up-to-date documentation for machine learning libraries. Its primary purpose is to help AI systems avoid hallucinations and generate more accurate responses when referencing ML frameworks and libraries.
Traditional AI systems trained on static datasets often have outdated information about rapidly evolving ML libraries. MCP solves this problem by creating a standardized way for AI systems to retrieve current documentation, ensuring they provide accurate information.
Key Benefits
Reduce Hallucinations
By providing AI systems with accurate documentation, MCP significantly reduces the risk of generating incorrect information about ML libraries.
Always Current
Documentation is synchronized with the latest library versions, ensuring AI systems don't reference outdated methods or parameters.
Semantic Search
Find relevant documentation across libraries using natural language queries instead of exact matches.
Code Examples
Access verified, working code examples that demonstrate proper usage of library features.
Easy Integration
Simple API and SDKs make it easy to integrate MCP into existing AI systems and workflows.
Cross-Library Context
MCP provides contextual information across multiple libraries, improving AI responses for complex topics.
Supported Libraries
MCP currently supports documentation for the following popular ML libraries, with more being added regularly:
Ready to improve your AI?
Get started with MCP today to enhance your AI system with accurate ML library documentation.