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LLM Wiki Tools
A compounding wiki your LLM builds and maintains.
LLM Wiki Tools turns your documents into a living wiki maintained by Claude. Upload PDFs, office docs, notes, and web articles, connect Claude through MCP, and let it compile your sources into cited Markdown pages, concept pages, entity pages, tables, diagrams, and cross-references.
I built LLM Wiki Tools because I kept running into the same problem with AI document workflows: the answer might be useful, but the work disappears into chat history.
Most file chat and RAG products retrieve chunks, answer the current question, and then start over next time. That is helpful, but it does not feel like building knowledge.
LLM Wiki Tools takes a different approach. You upload your sources, connect Claude through MCP, and let Claude build a persistent wiki on top of your documents. The raw sources stay immutable. The wiki layer becomes the place where Claude writes summaries, entity pages, concept pages, comparison tables, citations, cross-references, and notes about contradictions.
The goal is simple: AI-assisted reading should compound.
What you can do with it:
- Upload papers and ask Claude to build a cited research overview. - Turn course readings into a study wiki with a glossary and concept index. - Compare reports and save the result as a source-grounded table. - Ask Claude to lint the wiki for contradictions, stale claims, orphan pages, and missing citations. - Connect Claude, Cursor, custom agents, or scripts through MCP/API keys.
Every plan includes the core product: OCR, citations, full-text search, semantic search, and MCP access. Paid tiers mainly increase capacity.
I would especially love feedback from researchers, students, writers, analysts, and builders using Claude or MCP in serious document-heavy workflows.
Thanks for checking it out.
About LLM Wiki Tools on Product Hunt
“A compounding wiki your LLM builds and maintains.”
LLM Wiki Tools was submitted on Product Hunt and earned 0 upvotes and 1 comments, placing #9 on the daily leaderboard. LLM Wiki Tools turns your documents into a living wiki maintained by Claude. Upload PDFs, office docs, notes, and web articles, connect Claude through MCP, and let it compile your sources into cited Markdown pages, concept pages, entity pages, tables, diagrams, and cross-references.
On the analytics side, LLM Wiki Tools competes within Productivity, Notes and Artificial Intelligence — topics that collectively have 1.1M followers on Product Hunt. The dashboard above tracks how LLM Wiki Tools performed against the three products that launched closest to it on the same day.
Who hunted LLM Wiki Tools?
LLM Wiki Tools was hunted by Lok. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
For a complete overview of LLM Wiki Tools including community comment highlights and product details, visit the product overview.
Lok
Hi Product Hunt,
I built LLM Wiki Tools because I kept running into the same problem with AI document workflows: the answer might be useful, but the work disappears into chat history.
Most file chat and RAG products retrieve chunks, answer the current question, and then start over next time. That is helpful, but it does not feel like building knowledge.
LLM Wiki Tools takes a different approach. You upload your sources, connect Claude through MCP, and let Claude build a persistent wiki on top of your documents. The raw sources stay immutable. The wiki layer becomes the place where Claude writes summaries, entity pages, concept pages, comparison tables, citations, cross-references, and notes about contradictions.
The goal is simple: AI-assisted reading should compound.
What you can do with it:
- Upload papers and ask Claude to build a cited research overview.
- Turn course readings into a study wiki with a glossary and concept index.
- Compare reports and save the result as a source-grounded table.
- Ask Claude to lint the wiki for contradictions, stale claims, orphan pages, and missing citations.
- Connect Claude, Cursor, custom agents, or scripts through MCP/API keys.
Every plan includes the core product: OCR, citations, full-text search, semantic search, and MCP access. Paid tiers mainly increase capacity.
I would especially love feedback from researchers, students, writers, analysts, and builders using Claude or MCP in serious document-heavy workflows.
Thanks for checking it out.