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LLM Wiki Tools turns PDFs, documents, notes, and web articles into a living, cross-referenced wiki that Claude can search, write, update, and maintain through MCP.
Traffic, search & AI signals for llmwiki.tools.
Third-party traffic estimate · Updated Aug 9, 2026
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LLM Wiki Tools turns your PDFs, documents, notes, and web articles into a living, cross-referenced wiki that Claude builds and maintains for you via MCP.
LLM Wiki Tools is an AI-powered knowledge base that transforms uploaded documents into a durable, cross-referenced wiki. It accepts PDFs (with OCR), Word documents, PowerPoint decks, Markdown files, plain text, and web articles as raw sources. Claude, connected via the Model Context Protocol (MCP), reads these sources and writes Markdown wiki pages — including overviews, entity pages, concept pages, summaries, comparison tables, diagrams, logs, and cited notes. Raw sources remain immutable; the wiki is a separate, revisable layer. The product is offered by LLM Wiki Tools (llmwiki.tools).
Follow four steps: (1) Create a wiki at LLM Wiki Tools. (2) Upload your sources — PDFs, Office docs, Markdown, or web captures. (3) Connect Claude via MCP by copying the connector config from Settings into Claude.ai. (4) Tell Claude what to do, e.g., "Read these papers and build an entity page for each method." Claude then searches, reads, writes, and lints the wiki automatically.
RAG retrieves fragments on each query and forgets them. ChatGPT with files holds context for a single session. An LLM Wiki writes a durable layer — summaries, concept pages, contradictions — that survives across sessions and compounds.
It connects to Claude via MCP. You use Claude.ai as the interface; the MCP server gives it tools to search, read, write, and delete inside your vault.
No. Sign up at LLM Wiki Tools, upload sources, paste one MCP config into Claude.ai, and you are finished. There is no local setup required.
LLM Wiki Tools is a faithful implementation of Andrej Karpathy's LLM Wiki proposal — the three-layer model of raw sources, the LLM wiki, and the schema, with ingest / query / lint as the core operations.