footnote for LangChain
com.brick-byte/footnote
Every number in an AI answer traces back to the rows that produced it.
client:LangChain
transport:stdio
runtime:npm
Install footnote in LangChain
pip install langchain-mcp-adapters
from langchain_mcp_adapters.client import MultiServerMCPClient
client = MultiServerMCPClient({
"footnote": {
"transport": "stdio",
"command": "npx",
"args": ["-y", "@brick-byte/footnote"],
}
})
tools = await client.get_tools()
get_tools() returns the server's tools as LangChain tools, ready for an agent. LangChain docs