How to Use the Vimeo MCP in LlamaIndex

Q: How does LlamaIndex handle Vimeo data? Is it live?

It indexes the output. When you run searchvideos, the results are written to your vector store, making them part of a persistent knowledge base that persists beyond the session.

Q: Can I use LlamaIndex for video metadata from Vimeo?

Absolutely. You can call getvideo and index all its fields—the description, dimensions, ownership—making them queryable alongside your corporate documents.

Q: What if I want to check my groups on Vimeo with LlamaIndex?

You use the MCP Server's listgroups tool. The resulting list of group names and IDs gets indexed, allowing you to query them semantically later for reports or reviews.

Ground complex Vimeo insights using LlamaIndex's knowledge graph.

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Connect Vimeo MCP to LlamaIndex

Create your Vinkius account to connect Vimeo 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 Vimeo with LlamaIndex

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Indexing Search Results

When you run `search_videos`, the results don't just pass through—LlamaIndex indexes them. This means the output of your API search becomes a searchable knowledge base for later queries. You can query past sessions or configurations and get answers grounded in actual Vimeo data, not just general information.

Combining RAG with Video Details

Build Retrieval-Augmented Generation (RAG) apps that combine live API data with your own docs. Use `get_video` to pull specific video details—the title, description, and owner—and index them alongside other documents. Your agent can then answer questions like, 'What was the original upload date for videos in Folder X?' by cross-referencing indexed metadata.

Auditing Account Structure History

LlamaIndex lets you query your account structure over time. By indexing results from `list_folders` or `list_groups`, you can ask complex questions about how the project organization changed. This is perfect for compliance audits, letting you verify that certain projects were moved or deleted on a specific date.

Setup guide

Set up Vimeo 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 Vimeo 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 Vimeo tools.",
)
response = await agent.run("List recent Vimeo data")

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Vimeo. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about Vimeo MCP in LlamaIndex

It indexes the output. When you run `search_videos`, the results are written to your vector store, making them part of a persistent knowledge base that persists beyond the session.

Absolutely. You can call `get_video` and index all its fields—the description, dimensions, ownership—making them queryable alongside your corporate documents.

You use the MCP Server's `list_groups` tool. The resulting list of group names and IDs gets indexed, allowing you to query them semantically later for reports or reviews.

Yes. You pass the tools directly into a FunctionAgent setup. This ensures your agent can access the latest API calls, like `list_channels`, and index those results.

The server touches video metadata (titles, descriptions), user identifiers, and organizational structures (folders/showcases). These structured outputs form the core of your searchable index.

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