pytorch-mcp

pytorch-mcp is a full-featured MCP server for PyTorch documentation workflows. It indexes the repository's local docs/ tree and exposes search, page retrieval, symbol lookup, code-example extraction, troubleshooting, and question-answering tools that an LLM can use to help developers work with PyTorch.

• Uses docs/ as the source of truth for documentation-aware tools.
• Indexes local Markdown and reStructuredText PyTorch docs.
• Searches by workflow, concept, topic, or exact symbol such as torch.compile.
• Returns grounded snippets, headings, and page metadata for follow-up exploration.
• Extracts code examples from relevant docs pages.
• Understands declared Torch ecosystem libraries from pyproject.toml and inspects runtime availability.
• Recommends reading paths for tasks like training, compilation, data loading, profiling, and troubleshooting.
• Exposes MCP tools, resources, prompts, plus HTTP health/readiness routes.

The MCP server is intended to behave like a PyTorch development copilot:

• Use local docs/ content as the authoritative source for documentation-aware answers.
• Inspect declared and installed Torch libraries before making environment-specific recommendations.
• Use planning, template-generation, code-inspection, and runtime-validation tools to help developers build models faster.
• Keep debugging and optimization advice grounded in retrieved docs, parsed traces, profiler data, and runtime checks when available.

• list_doc_topics
• search_docs
• get_doc_page
• get_symbol_reference
• extract_code_examples
• answer_pytorch_question
• recommend_docs
• troubleshoot_pytorch
• plan_model_build
• assemble_training_stack
• generate_training_loop_template
• generate_task_specific_template
• generate_training_project_template
• review_training_code
• suggest_model_architecture
• choose_loss_and_optimizer
• optimize_data_pipeline
• `diagnose_trainin