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Centralize knowledge, run semantic search, ingest documents, and generate RAG answers.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "mcp-knowledge-server" yet — see the docs or source repo.
Based on the product docs, FAQs, and meeting notes in the knowledge base, answer: what are the key steps in the first-week activation flow for new users? Include citations.
A knowledge-grounded answer summarizing the key steps with traceable source citations.
Ingest the PDFs, Markdown files, and text files in this project folder into the knowledge base, and add metadata tags by department and topic for later retrieval.
Documents are ingested and indexed, with import results, processing status, and structured metadata for retrieval.
Search the knowledge base for content most related to 'user churn early warning metrics', rank by relevance, and return the top 5 results with summaries.
A semantically ranked result list with document snippets, summaries, and relevance information.
Secure internal knowledge retrieval with permission-aware access control and citation enforcement.
Turn unstructured documents into a searchable knowledge base for AI agents.
Index a knowledge base into Chroma and retrieve relevant document fragments.
Aggregate encyclopedic, research, and technical docs into one AI-ready knowledge interface.
Production-ready MCP server for query normalization, retrieval, and RAG prompt building.
Give AI agents semantic memory and web search for stronger retrieval and reasoning.