LangChain ACLM Lab Interpreter marker_reference
How to: LangChain ACLM Lab Interpreter marker_reference
Add ACLM Lab Interpreter 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({
"aclm-lab-interpreter": {
"transport": "streamable_http",
"url": "https://web-production-ae61.up.railway.app/mcp/aclm-lab-interpreter",
}
})
tools = await client.get_tools()
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02
Save the file and restart LangChain.
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03
Ask for something marker_reference does. LangChain lists the server's tools on connect and calls marker_reference itself — you do not invoke it by name.
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04
If nothing happens, check the server is running and that ACLM Lab Interpreter's identifier in your config matches the one above exactly.
get_tools() returns the server's tools as LangChain tools, ready for an agent. LangChain docs
Same tool, other clients
This list was read from the server itself, by connecting to it and calling tools/list on 24 September 2026. It is what
the server actually exposes, not what its listing claims.
Mutating and Read-only are read off each tool's name, not its schema — a hint, not a guarantee. The registry stores tool names only; connect the server for its live schemas.