Which integrations are available for this server?

Allows the Gemma 4 agent running on Ollama to use retrieval tools via MCP for answering questions about book content.

How do I use scholar-mcp?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@scholar-mcp Find passages about the concept of free will in my philosophy ebooks" That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

de en es ja ko ru zh

scholar-mcp

by unlomtrois

Overview Schema Related Servers Score Discussions

Python

Local

little-librarian

A local MCP server that indexes .epub files and exposes semantic search tools backed by pplx-embed-context-v1 (late chunking) and Qdrant.

MCP client (Claude Desktop, Claude Code, …)
     │
     ▼  tool calls via MCP
 server.py
  pplx-embed-context-v1-0.6b  +  Qdrant (local)

Files

File	Role
`server.py`	MCP server — epub ingestion, embedding, search, Qdrant storage
`code_librarian.py`	Separate MCP server for code (AST-based chunking)

Why pplx-embed-context-v1

Uses late chunking: all chunks from a chapter go through a single forward pass, so each chunk embedding captures full document context without needing a doc-prefix at inference time. Scores 81.96 nDCG@10 on ConTEB.

Quick start

# 1. install
pip install -e .

# 2. ingest your library (runs embedding, then exits)
HF_HUB_OFFLINE=0 python server.py --index ./library

# 3. start the MCP server
python server.py

# optional: preload the model at startup
python server.py --preload

MCP tools

Tool	Description
`search(query, top_k)`	Semantic search, returns top-k passages with scores
`search_groups(query, group_by, limit, group_size)`	Search grouped by `"book"` or `"chapter"` — best for cross-volume research
`get_passage(book, chapter, max_chars)`	Retrieve full text for a book/chapter
`list_books()`	List all indexed books with chapter counts
`collection_stats()`	Qdrant collection info (point count, vector size)
`library_stats()`	Full content breakdown: books, chapters, chunks per book, avg chunk length
`get_device()`	Show which device (CPU/GPU) is used for embeddings

Claude Desktop config

{
  "mcpServers": {
    "little-librarian": {
      "command": "python",
      "args": ["/path/to/server.py"]
    }
  }
}

Hardware guidance

Setup	Min VRAM
CPU only	0 GB
GPU (pplx-embed-0.6b)	~2 GB

This server cannot be installed

license - not found

quality - not tested

maintenance

How are these scores calculated?

Resources

GitHub Repository

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