Librarian is a MCP (Model Context Protocol) Server that allows any LLM with a compatible MCP client to query Wikipedia for information. It can be
Connect Librarian to Claude, Cursor or any other MCP client and it stops being a tab you switch to. Librarian is a MCP (Model Context Protocol) Server that allows any LLM with a compatible MCP client to query Wikipedia for information. It can be configured to automatically fact-check information without requiring explicit user requests. The librarian mcp server is what makes that connection.
Setup follows the usual MCP pattern — install or clone the server, register it in your client's configuration file, restart the client. The configuration blocks on this page cover the common clients.
Configuration is passed through the environment: PYTHONPATH. Treat anything key-shaped as a real credential — scope it to the minimum the server needs, and rotate it if it ever lands in a shared config.
Among the AI and media services options, the useful question is rarely "what can it do" but "what does it cost you to run" — permissions, credentials, and how much of your context its toolset consumes. It is maintained by mlziade; worth a glance at recent repository activity before you build anything load-bearing on it.
SyncDev reviews every entry in this directory against the project's own documentation before publishing, and revisits them as servers change.
**For macOS/Linux:**
```json
{
"mcpServers": {
"librarian": {
"command": "uv",
"args": ["--directory", "/path/to/your/librarian", "run", "python", "librarian_stdio.py"],
"env": {
"PYTHONPATH": "/path/to/your/librarian"
}
}
}
}Configuration as documented by the project. Restart the client after saving.
| Variable | Description | Required |
|---|---|---|
| PYTHONPATH | Filesystem location the server is allowed to use. | Optional |
Build a programmable telecommunications stack for connecting telephony services with the Internet via a cloud-based utility.
Search built for AI, not humans — semantic web search that returns model-ready content, plus code context.
Answers, not links — delegate questions to Perplexity's search-grounded models and get cited responses back.
Give your assistant a voice — text-to-speech, voice cloning and audio tools from the ElevenLabs API.
Give your assistant a real code sandbox — isolated cloud VMs for actually running the code it writes.
The ML hub in your context window — search models, datasets, papers and run Spaces from the official server.