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

Q: Can I query video analytics using Loom (Async Video Messaging) and LlamaIndex?

Yes, you can index the output of getvideoanalytics into your vector store. This allows your agent to answer questions about which videos are getting the most views or where viewers drop off.

Q: Does the Loom (Async Video Messaging) MCP Server support folder organization in LlamaIndex?

Yes, the server exposes listfolders which allows your indexing pipeline to group documents by their actual workspace folders. This keeps your RAG search results organized and contextual.

Q: How do I update video titles from my LlamaIndex agent?

You can use the updatevideo tool directly from your agent when it detects a video title that doesn't match its indexed content. This keeps your source library and your search index in sync.

Index your Loom (Async Video Messaging) transcripts into LlamaIndex using this high-performance MCP Server.

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Connect Loom (Async Video Messaging) MCP to LlamaIndex

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

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Build RAG pipelines with Loom (Async Video Messaging)

LlamaIndex lets you index the text of your team recordings directly into a searchable vector store. By calling `get_transcript`, your agent extracts the spoken content of your videos and processes it into queryable nodes. Instead of scrubbing through hours of video, you can ask your agent about specific decisions made in past meetings. The agent queries the index and links you directly to the source video using `get_video`.

Semantic search across your MCP Server folders

This MCP Server allows your agent to map your entire video library structure. Your LlamaIndex pipeline can call `list_folders` and `list_videos` to build a semantic map of where your team stores its knowledge. Users can search for topics across folders without knowing the exact title of the recording. The agent matches the semantic query to the indexed metadata and returns the correct folder path.

Index viewer engagement and workspace members

Keep your knowledge base updated with real-time usage data. LlamaIndex can pull active viewer metrics using `get_video_analytics` and store them alongside your video documents. You can also index your team structure with `list_workspace_members` to see which departments produce the most viewed content. It helps you find the actual experts in your company based on their recorded output.

Setup guide

Set up Loom (Async Video Messaging) MCP in LlamaIndex

Prerequisites

Python 3.10+ installed
llama-index-tools-mcp package
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai. The MCP tools package provides BasicMCPClient and McpToolSpec.
2

Connect with BasicMCPClient
Point BasicMCPClient to your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports SSE and Streamable HTTP transports.
3

Convert to LlamaIndex tools
Call mcp_tool_spec.to_tool_list_async() to convert all Loom (Async Video Messaging) MCP tools into native FunctionTool objects that any LlamaIndex agent can use.
4

Run with any LLM
Create a FunctionAgent with the tools and your preferred LLM. Swap OpenAI for Anthropic, Gemini, or any LlamaIndex-supported provider.

agent.py

from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

# Connect to the MCP
mcp_client = BasicMCPClient(
    "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)
mcp_tool_spec = McpToolSpec(client=mcp_client)

# Convert MCP tools to LlamaIndex tools
tools = await mcp_tool_spec.to_tool_list_async()

# Create and run the agent
agent = FunctionAgent(
    tools=tools,
    llm=OpenAI(model="gpt-4o"),
    system_prompt="You have access to Loom (Async Video Messaging) tools.",
)
response = await agent.run("List recent Loom (Async Video Messaging) data")

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

Use McpToolSpec to load the MCP tools into your LlamaIndex agent, then call `get_transcript` to fetch the text. You can then parse this text into documents and build your vector index.

Yes, you can index the output of `get_video_analytics` into your vector store. This allows your agent to answer questions about which videos are getting the most views or where viewers drop off.

Yes, the server exposes `list_folders` which allows your indexing pipeline to group documents by their actual workspace folders. This keeps your RAG search results organized and contextual.

You can use the `update_video` tool directly from your agent when it detects a video title that doesn't match its indexed content. This keeps your source library and your search index in sync.

All transcript text and video metadata fetched via this MCP Server are handled in-memory and sent directly to your local vector store. No external party can access your private meeting transcripts or workspace member lists.

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