Model Context Protocol
Always up-to-date ML library documentation for AI consumption to reduce hallucinations and provide accurate information.
import torch
from transformers import AutoModel, AutoTokenizer
# Load model and tokenizer
model_name = "bert-base-uncased"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModel.from_pretrained(model_name)
# Encode text
text = "MCP helps you understand ML libraries"
inputs = tokenizer(text, return_tensors="pt")
outputs = model(**inputs)
# Get embeddings
embeddings = outputs.last_hidden_stateFind What You Need
Access the latest documentation through our AI-friendly API or use our semantic search
Featured Libraries
Explore popular machine learning libraries with comprehensive documentation and examples
TensorFlow is an end-to-end open-source platform for machine learning. It has a comprehensive, flexible ecosystem of tools, libraries, and community resources.
PyTorch is an optimized tensor library for deep learning using GPUs and CPUs. It features a dynamic computational graph and is widely used in research.
Hugging Face Transformers provides thousands of pre-trained models for natural language processing, computer vision, and more.
Why Use MCP?
MCP ensures AI systems have access to accurate, up-to-date ML library documentation
Our documentation is continuously synchronized with official sources to ensure AI systems don't reference outdated information.
By providing accurate, structured library documentation, MCP helps AI systems reduce hallucinations when generating code or explaining concepts.
Our API is designed specifically for AI consumption, making it easy for language models to retrieve and understand documentation context.