F
license-
qualityD
maintenanceLocal MCP server that provides semantic search (RAG) over code repositories, enabling AI clients like Claude and Gemini to access project context without manual re-upload.
Last updated
scholar-mcp
by unlomtrois
Allows the Gemma 4 agent running on Ollama to use retrieval tools via MCP for answering questions about book content.
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@scholar-mcpFind 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.
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 |
| MCP server — epub ingestion, embedding, search, Qdrant storage |
| Separate MCP server for code (AST-based chunking) |
Related MCP server: ragi
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 --preloadMCP tools
Tool | Description |
| Semantic search, returns top-k passages with scores |
| Search grouped by |
| Retrieve full text for a book/chapter |
| List all indexed books with chapter counts |
| Qdrant collection info (point count, vector size) |
| Full content breakdown: books, chapters, chunks per book, avg chunk length |
| 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
Maintenance
–Maintainers
–Response time
–Release cycle
–Releases (12mo)
Commit activity
Resources
Unclaimed servers have limited discoverability.
Looking for Admin?
If you are the server author, to access and configure the admin panel.
Related MCP Servers
- AlicenseAqualityBmaintenanceLocal-first RAG indexing and semantic search MCP server. Enables document retrieval and context-aware queries using local embedding models.Last updated325MIT
- Alicense-qualityCmaintenanceMCP server for Qdrant vector database with local BERT embeddings. Enables semantic search and vector storage operations through natural language.Last updatedMIT
- Alicense-qualityDmaintenanceA knowledge base MCP server backed by Qdrant vector database with local embeddings for semantic search and document management.Last updated111ISC
Related MCP Connectors
Local-first RAG engine with MCP server for AI agent integration.
Agent-native MCP server over the public saagarpatel.dev corpus. Read-only, stateless.
Remote ChromaDB vector database MCP server with streamable HTTP transport
Latest Blog Posts
- Who's Calling? MCP Hosts Are an Identity Blind Spot (And the Spec Knows It)By Om-Shree-0709 on .mcpAgent IdentityOAuth 2.1
- Your AI Chatbot Just Exposed Your CEO's Salary to an InternBy Om-Shree-0709 on .Agent IdentityMCP SecurityOAuth Delegation
- Why MCP Servers Need Execution Sandboxing (And Why Your Current Stack Isn't Enough)By Om-Shree-0709 on .Agentic AiPrompt InjectionWebAssembly
MCP directory API
We provide all the information about MCP servers via our MCP API.
curl -X GET 'https://glama.ai/api/mcp/v1/servers/unlomtrois/little-librarian'
If you have feedback or need assistance with the MCP directory API, please join our Discord server