Scholar Rag MCP Server

scholar-rag-mcp

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.

Features

• 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 (doc_id, section, year, journal, ...) evaluated inside the Qdrant index. 100k-chunk p95 query latency < 1s (see docs/perf-report.md).
• Async jobs: create_kb/add_document are background jobs with progress queryable via get_job; safe to restart (interrupted jobs are recovered and skipped on re-run).
• Context-safe reading: paginated get_document_text with hard size caps; outline first, pages on demand.
• 11 MCP tools over stdio: list_kbs, create_kb, delete_kb (two-phase), add_document, remove_document, get_document, get_document_text, list_documents, search_documents, search_chunks, get_job.
• Self-contained storage: knowledge bases live under a single data directory (~/.scholar-rag); Qdrant is either auto-launched (single binary, version-pinned) or connected to an external instance.

Installation

Requires pixi. From the repository root:

pixi install                      # installs the default environment

The project defines three pixi environments, each serving a different purpose:

Verify your environment with the built-in doctor:

pixi run python scripts/doctor.py

Model deployment

Environment ('chat', 'embed' and 'rerank' clients) expects OpenAI-compatible HTTP endpoints. scripts/serve_models.sh launches three vLLM instances for the reference model set:

# point *_MODEL at your local model directories, then:
bash scripts/serve_models.sh

SCHOLAR_RAG_CHAT_MODEL, SCHOLAR_RAG_EMBED_MODEL and SCHOLAR_RAG_RERANK_MODEL are required - the script exits with a message listing them if any is unset. Each value must be an absolute path to a local HuggingFace model directory; vLLM serves the model under that same path, so the matching client settings must hold the identical value (the served name equals the path). Replace the /path/to/... placeholders in .env.example accordingly. Ports (CHAT_PORT/EMBED_PORT/RERANK_PORT) and GPU ids remain optional with working defaults.

The script pins the exact vLLM flags verified for these models (embed via --convert embed, rerank via --convert classify + a custom chat template). Model load takes several minutes; the script polls health until all three answer.

Minimal environment

Start from .env.example and set at least the model endpoints (placeholder paths - replace with your own, and make sure the served model names match exactly):

SCHOLAR_RAG_DATA_DIR=~/.scholar-rag
SCHOLAR_RAG_QDRANT_STORAGE_DIR=~/.local/share/scholar-rag/qdrant

SCHOLAR_RAG_CHAT_BASE_URL=http://127.0.0.1:8101/v1
SCHOLAR_RAG_CHAT_MODEL=/path/to/models/Qwen/Qwen3-8B

SCHOLAR_RAG_EMBED_BASE_URL=http://127.0.0.1:8102/v1
SCHOLAR_RAG_EMBED_MODEL=/path/to/models/Qwen/Qwen3-VL-Embedding-2B

SCHOLAR_RAG_RERANK_BASE_URL=http://127.0.0.1:8103/v1
SCHOLAR_RAG_RERANK_MODEL=/path/to/models/Qwen/Qwen3-VL-Reranker-2B

The embed model dimension is recorded in kb_meta.json at kb creation, so changing the embedding model later requires a new kb.

MCP client setup

Start the server entry point directly to make sure it runs:

pixi run scholar-rag-mcp

Claude (Claude Desktop / claude CLI)

{
  "mcpServers": {
    "scholar-rag-mcp": {
      "command": "pixi",
      "args": ["run", "scholar-rag-mcp"]
    }
  }
}

opencode

{
  "$schema": "https://opencode.ai/config.json",
  "mcp": {
    "scholar-rag-mcp": {
      "type": "local",
      "command": ["pixi", "run", "scholar-rag-mcp"]
    }
  }
}

Tools

Data layout

<data_dir>/                     # SCHOLAR_RAG_DATA_DIR, default ~/.scholar-rag
├── kbs/<kb_name>/
│   ├── kb_meta.json            # dimension, chunk config, schema version
│   ├── catalog.sqlite3         # documents / authors / keywords / chunks + FTS5
│   └── documents/<doc_id>/     # source.pdf, full_text.md, sections.json
├── cache/parse/                # MinerU markdown cache, keyed by content hash
├── cache/resolver/             # annotation resolver cache, keyed by content hash
├── jobs.sqlite3                # async job history
└── bin/                        # auto-downloaded Qdrant binary (v1.12.5)

Qdrant storage lives outside data_dir at QDRANT_STORAGE_DIR (default ~/.local/share/scholar-rag/qdrant) - it must be on a local filesystem, not a 9p/network mount.

Two-phase kb deletion

delete_kb never deletes on the first call with the wrong arguments by accident:

1. Call delete_kb(kb="...") - returns kb statistics plus a 10-minute confirm_token.
2. Call delete_kb(kb="...", confirm_token="<token>") to actually delete the Qdrant collection, kb directory and its job history.

Development

pixi run lint          # ruff check src tests
pixi run typecheck     # mypy src
pixi run test          # pytest (unit + integration, no e2e/perf)
pixi run -e mineru pytest tests/e2e/smoke.py -v -m e2e   # real end-to-end smoke
python tests/perf/bench_query.py                          # query latency benchmark (writes docs/perf-report.md)

Release notes

For known limitations and upgrade guidance see docs/handoffs/release-notes-v0.1.0.md.

Known constraints worth repeating:

• 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 pixi environment because its transformers version is mutually exclusive with the vLLM one. PDF parsing thus prefers pixi run -e mineru.
• MinerU weights (~3.2 GB) download on first parse into ~/.cache/modelscope/.
• Metadata title heuristic: titles are only picked locally when the MinerU markdown starts with an #/## heading, so a leading ## Abstract (etc.) can be misread as the title. This affects the local-heuristic metadata tier only; the CrossRef tier (used when a DOI is found) normally corrects it.
• Tool dispatch: unknown extra arguments to a tool are silently ignored rather than rejected.
• 9p storage limit: Qdrant storage must be on a local filesystem.