How to Use the Assembly MCP in LangChain

Q: Can a LangChain agent search Assembly notes automatically?

Yes. Your agent can call listnotes to scan all entries or getnote to read a specific one. This allows the model to pull context from recent meetings and use it in downstream chain steps.

Feed real-time Assembly workspace and client data directly into your LangChain reasoning loops.

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Connect Assembly MCP to LangChain

Create your Vinkius account to connect Assembly to LangChain and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup Assembly with LangChain

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Map Assembly workspaces in LangChain chains

Your LangChain agent can now pull raw workspace setups and company details on the fly using this MCP Server. By exposing `list_workspaces` and `get_workspace`, the agent maps out where your teams are actually working without you hardcoding IDs. It gets the current state of play directly from Assembly. Output from these tools feeds straight into your next chain link. If the agent needs to audit active workspaces, it grabs the list, filters by active users via `list_users`, and writes the summary. You see every step of this data flow in your LangSmith traces.

ReAct agents that query and cross-reference clients

Stop writing custom API glue code for your account management flows. This MCP Server lets your ReAct agents run multi-step loops to link companies with their respective notes. The agent invokes `list_companies` to find a match, then automatically pulls relevant context. When a client relationship changes, your agent uses `get_client` and `get_note` to build a chronological history. It decides which tool to call based on what the previous tool returned. You get a clean, trace-ready pipeline that runs on actual facts.

Traceable client and company lookups

LangChain excels at running structured pipelines, but it needs reliable data. By linking `get_company` and `list_clients` directly to your model, you eliminate the guesswork. The model pulls exact records instead of relying on outdated training data. Because this runs through Vinkius, your connection stays secure and fast. You can audit every single tool call in LangSmith, checking exactly how many tokens your agent used to find a specific user with `get_user`.

Setup guide

Set up Assembly MCP in LangChain

Prerequisites

Python 3.10+ installed
langchain-mcp-adapters + langgraph packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install langchain-mcp-adapters langgraph langchain-openai. The MCP adapters package converts MCP tools into native LangChain BaseTool objects.
2

Connect via HTTP transport
Use MultiServerMCPClient with "transport": "http" pointing to your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Create a ReAct agent
Pass the discovered tools to create_react_agent() from LangGraph. The agent automatically routes Assembly tool calls through the MCP protocol.
4

Run with any LLM
Swap ChatOpenAI for ChatAnthropic, ChatGoogleGenerativeAI, or any LangChain-compatible model. The MCP tools work identically across all providers.

agent.py

from langchain_mcp_adapters.client import MultiServerMCPClient
from langgraph.prebuilt import create_react_agent
from langchain_openai import ChatOpenAI

async with MultiServerMCPClient({
    "assembly-mcp": {
        "transport": "http",
        "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
    }
}) as client:
    tools = client.get_tools()

    agent = create_react_agent(
        ChatOpenAI(model="gpt-4o"),
        tools,
    )
    result = await agent.ainvoke({
        "messages": "List recent Assembly transactions"
    })
    print(result["messages"][-1].content)

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Common questions about Assembly MCP in LangChain

You install the LangChain MCP adapters and initialize the client with your Vinkius endpoint. From there, call the get tools method and pass them directly to your agent constructor. It takes less than five minutes to get running.

Yes. Your agent can call `list_notes` to scan all entries or `get_note` to read a specific one. This allows the model to pull context from recent meetings and use it in downstream chain steps.

Absolutely, every tool call is fully visible. When your agent runs `list_users` or `get_client`, you can see the exact input, output, and latency inside your LangSmith dashboard. It makes debugging complex multi-agent chains straightforward.

You can mix this with any of LangChain's existing integrations. For example, your agent can pull data using `get_company` and immediately write that data to a local database or vector store.

Your Assembly notes, company profiles, client records, workspaces, and user details are protected by Vinkius's isolated sandbox environment. No data is stored on our servers, and all API calls run through an ephemeral, zero-trust connection. Your sensitive workspace information never trains public models.

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