LangChain Imperium RAG-Diet — Web & Document Context Distiller (85-97% Token Savings) Prompt/bm25-budget-research
How to: LangChain Imperium RAG-Diet — Web & Document Context Distiller (85-97% Token Savings) bm25-budget-research
Add Imperium RAG-Diet — Web & Document Context Distiller (85-97% Token Savings) to LangChain
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01
Open pip install langchain-mcp-adapters and merge this in. Keep any servers already there.
from langchain_mcp_adapters.client import MultiServerMCPClient
client = MultiServerMCPClient({
"rag-diet": {
"transport": "streamable_http",
"url": "https://rag-diet.agentdiet.dev/mcp",
}
})
tools = await client.get_tools()
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02
Save the file and restart LangChain.
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03
bm25-budget-research is surfaced through the client's prompt API, fetched by name. Unlike a tool, you invoke it deliberately — LangChain will not call it for you.
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04
If it does not appear, check that Imperium RAG-Diet — Web & Document Context Distiller (85-97% Token Savings) is connected and that you are looking at its prompts rather than its tools.
get_tools() returns the server's tools as LangChain tools, ready for an agent. LangChain docs
Same prompt, other clients
Read from the server itself, by connecting to it and calling prompts/list on 21 October 2026.
A listing does not declare its prompts, so asking is the only way to know them, and this
is what the server actually offers rather than what its listing claims. Names only are
stored; connect the server for each prompt's arguments.