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Index and query knowledge from URLs, PDFs, and TeX in VS Code.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "knowledge-mcp-server" yet — see the docs or source repo.
Use knowledge-mcp-server to index these sources: project documentation URLs, 3 PDF specification files, and 2 TeX papers. Then organize them into a searchable knowledge base by topic and list all imported sources.
A list of indexed sources, a topic-based knowledge base structure, and a summary of what can be queried next.
Using the indexed knowledge, answer: what are the system's core architecture, main dependencies, and deployment prerequisites? Provide concise conclusions and cite which documents each answer comes from.
A concise technical Q&A summary with source citations for quick understanding and traceability.
Based on the API specs and design documents in the knowledge base, generate an implementation plan usable in VS Code, including module breakdown, key function suggestions, and a README draft.
An implementation plan and documentation draft grounded in the indexed knowledge to speed up development.
Centralize knowledge, run semantic search, ingest documents, and generate RAG answers.
Aggregate encyclopedic, research, and technical docs into one AI-ready knowledge interface.
Turn unstructured documents into a searchable knowledge base for AI agents.
Index documents and retrieve relevant context for better LLM responses.
Search, retrieve, and answer questions from PDF documents with RAG.
Secure internal knowledge retrieval with permission-aware access control and citation enforcement.