How to Use the Loom (Async Video Messaging) MCP in LangChain

Q: Can my LangChain agent analyze transcripts using Loom (Async Video Messaging)?

Yes, your agent can call gettranscript within a chain to fetch the text of any recording. You can then pass that transcript to an LLM chain to generate summaries or action items automatically.

Q: How does LangSmith trace Loom (Async Video Messaging) tool calls in LangChain?

LangSmith logs every single execution of tools like getvideo or updatevideo in real-time. You can inspect the exact inputs, outputs, and latency of each video management operation.

Q: Can I manage folders with this MCP Server?

Absolutely. Your agent can call listfolders to map out your workspace structure and organize recordings into the right directories.

Chain Loom (Async Video Messaging) tools with LangChain agents using this MCP Server to automate video audits.

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Connect Loom (Async Video Messaging) MCP to LangChain

Create your Vinkius account to connect Loom (Async Video Messaging) 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 Loom (Async Video Messaging) with LangChain

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Build LangChain pipelines for video metadata

LangChain agents can coordinate multi-step workflows across your video library. By chaining `list_videos` with `get_transcript`, your agent can scan every recording, read the text, and flag missing action items without manual intervention. The agent reads the output of one step and feeds it directly into `update_video` to rename recordings based on their actual content. Every single step is traced in LangSmith, so you see exactly how the agent decided to rename each file.

Automate workspace cleanup with ReAct agents

Let your agent make decisions about stale content using live metrics. An agent can call `get_video_analytics` to check completion rates, identify videos with zero views over the last six months, and queue them for removal. If a video meets your deletion criteria, the agent triggers `delete_video` to keep your workspace clean. You get a clear, step-by-step log of why each recording was deleted, backed by real engagement data.

Audit team engagement using this MCP Server

Connect your video workspace to your internal databases using LangChain's extensive integration ecosystem. This MCP Server lets your agent pull active workspace data via `list_workspace_members` and cross-reference it with your HR directory. You can track who is watching onboarding videos by combining `get_video_analytics` and your team directory in a single, automated run. It removes the guesswork from tracking training compliance.

Setup guide

Set up Loom (Async Video Messaging) 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 Loom (Async Video Messaging) 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({
    "loom-async-video-messaging-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 Loom (Async Video Messaging) transactions"
    })
    print(result["messages"][-1].content)

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Common questions about Loom (Async Video Messaging) MCP in LangChain

Use the MultiServerMCPClient to connect to the MCP server endpoint and call get_tools(). You then pass these tools directly into your agent constructor to let it run tools like `list_videos` dynamically.

Yes, your agent can call `get_transcript` within a chain to fetch the text of any recording. You can then pass that transcript to an LLM chain to generate summaries or action items automatically.

LangSmith logs every single execution of tools like `get_video` or `update_video` in real-time. You can inspect the exact inputs, outputs, and latency of each video management operation.

Absolutely. Your agent can call `list_folders` to map out your workspace structure and organize recordings into the right directories.

Your Loom video metadata and transcripts are processed in a secure, ephemeral sandbox. No credentials or video files are stored permanently, ensuring your team recordings remain private.

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