Shelfmark for LangChain
io.github.dankaro-projects/shelfmark
Local, privacy-first document catalogue for AI agents: metadata-only discovery, no cloud, no RAG.
client:LangChain
transport:stdio
runtime:pypi
Install Shelfmark in LangChain
pip install langchain-mcp-adapters
from langchain_mcp_adapters.client import MultiServerMCPClient
client = MultiServerMCPClient({
"shelfmark": {
"transport": "stdio",
"command": "uvx",
"args": ["shelfmark"],
}
})
tools = await client.get_tools()
get_tools() returns the server's tools as LangChain tools, ready for an agent. LangChain docs