Academic-paper knowledge-base MCP server: PDF ingestion (MinerU), Qdrant vector search with reranking, two-phase KB management
View on GitHub ↗Status: preview release (v0.1.0). Interfaces and storage layout may change in future versions.
scholar-rag-mcp is a publishable academic-paper knowledge-base MCP tool. Point it at a folder of PDFs and it ingests each paper through a real parsing pipeline (MinerU), normalizes metadata, annotates section structure, chunks and embeds the text, and stores everything in Qdrant - after which an agent (or you) can semantically search chunks, run PubMed-style document queries, read full text section by section, add/remove single papers, and manage knowledge bases - all through 11 MCP tools over stdio. Embedding, annotation and re-ranking run on OpenAI-compatible model services (vLLM) with in-process fallbacks.
- Real ingestion pipeline: MinerU PDF parsing (python/cli/api backends) - metadata extraction (local heuristics, CrossRef, optional GROBID) - cleaning - section annotation - deterministic chunking (configurable 300/1500/100 chars) - embedding. - Fast retrieval at scale: embedding first-pass + cross-encoder re-rank, optional metadata filtering (docid, section, year, journal, ...) evaluated inside the Qdrant index. 100k-chunk p95 query latency ") to actually delete the Qdrant collection, kb directory and its job history.
For known limitations and upgrade guidance see docs/handoffs/release-notes-v0.1.0.md.
- Qdrant is pinned to v1.12.5 - it is the highest version that runs on glibc 2.35; auto-launch downloads it on first use. On glibc = 2.38 you may run a newer version, but the data format is not forward-compatible with older kbs in this release. - MinerU runs in its own pix
From the project README.
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