LangChain TokenLab MCP Server Resource/TokenLab Model Explorer
How to: LangChain TokenLab MCP Server TokenLab Model Explorer
Add TokenLab MCP Server to LangChain
-
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({
"tokenlab": {
"transport": "stdio",
"command": "npx",
"args": ["-y", "@tokenlabai/mcp-server"],
}
})
tools = await client.get_tools()
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02
Save the file and restart LangChain.
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03
TokenLab Model Explorer is surfaced through the client's resource API, read by URI. It is context to attach, not an action to run.
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04
If it does not appear, check that TokenLab MCP Server is connected and that you are looking at its resources rather than its tools.
get_tools() returns the server's tools as LangChain tools, ready for an agent. LangChain docs
Set these first
TokenLab MCP Server will not start until these are set, so it never reaches LangChain.
-
TOKENLAB_API_BASE -
TOKENLAB_API_KEY -
TOKENLAB_REQUEST_TIMEOUT_MS -
TOKENLAB_MCP_TOOL_PROFILE -
TOKENLAB_MCP_SCHEMA_MODE -
TOKENLAB_MCP_MAX_FILE_BYTES -
TOKENLAB_MCP_INLINE_BYTES -
TOKENLAB_ARTIFACT_DIR -
TOKENLAB_MANAGEMENT_TOKEN
Same resource, other clients
Read from the server itself, by connecting to it and calling resources/list on 18 October 2026.
A listing does not declare its resources, 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 resource's type and contents.