Extract PDFs into Markdown, RAG chunks, and cited tables.
Copy the install command and let the AI configure it · recommended for beginners
Please install the "doc.page PDF Extraction" MCP server from askskill: Run: claude mcp add --transport http 'page-doc-pdf-extract' 'https://doc.page/api/mcp'
Please use doc.page PDF Extraction to extract this PDF into Markdown and keep cited tables.
A ready-to-edit Markdown version with cited tables.
Please convert this PDF into RAG chunks suitable for retrieval-augmented generation.
Text chunks optimized for indexing and retrieval.
Please publish this document as a Doc Link with read stats.
A shareable document link with read statistics.
Researchers can use it to turn papers or reports into structured, searchable content. It outputs Markdown, RAG chunks, and cited tables for downstream analysis.
Teams building a knowledge base or retrieval Q&A system can first extract PDFs into index-friendly chunks. This makes them easier to plug into AI retrieval workflows.
When sharing documents with teammates or clients, you can publish a Doc Link with read stats. It helps track whether the document has been viewed.
According to the description, it can extract PDFs into Markdown, RAG chunks, and cited tables. It also supports publishing Doc Links with read stats.
It is useful for turning PDF materials into formats that are easier to edit, search, and cite. Common uses include research processing, knowledge-base ingestion, and document sharing.
The provided information does not list any extra prerequisites. For specific setup or permission requirements, see the source repository.
Read and analyze PDF documents for natural language Q&A and extraction.
Convert PDFs to Markdown per page with confidence scoring for RAG ingestion.
Extract structured data from academic PDFs with natural-language querying and batch workflows.
Extract PDF evidence, search citations, and manage Obsidian vault content.
Extract structured JSON tables from PDF URLs with reliable source citations.
Extract verifiable data from documents for Q&A, search, translation, and evidence storage.