How to Use the Dailymotion MCP in LlamaIndex

Q: How do I configure the Dailymotion MCP Server with LlamaIndex?

Install the tool package via pip install llama-index-tools-mcp. Initialize the client with BasicMCPClient, convert it using McpToolSpec, and pass the async tool list to your FunctionAgent.

Q: Can LlamaIndex index metadata from Dailymotion playlists?

Yes, the agent calls listplaylists to fetch playlist IDs and video counts, then feeds this data into your vector index. This allows your RAG application to answer queries about how your Dailymotion videos are grouped.

Q: How does LlamaIndex prevent hallucinations when discussing Dailymotion video performance?

The agent uses getvideo to pull live views and stream URLs directly from Dailymotion. By grounding its answers in this real-time tool output rather than static training data, the agent avoids generating incorrect statistics.

Q: Can I filter which tools are exposed to my LlamaIndex agent?

Yes, you can use the allowedtools filter when setting up the client. If you only want the agent to read data, you can expose listvideos and block write tools like deletevideo entirely.

Index Dailymotion video metrics and metadata into your LlamaIndex knowledge base with this MCP server.

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Connect Dailymotion MCP to LlamaIndex

Create your Vinkius account to connect Dailymotion 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 Dailymotion with LlamaIndex

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Index Dailymotion video data directly into LlamaIndex

The `list_videos` tool pulls your video IDs, titles, durations, and view counts directly into your local environment. LlamaIndex embeds the output of this tool directly into your vector index, grounding your RAG agent in real-time Dailymotion views. Instead of guessing which video needs optimization, your agent queries the index to find low-performing assets. It combines offline documents with live Dailymotion metrics to suggest title updates based on past performance.

Query public videos using LlamaIndex and Dailymotion

The `search_videos` tool queries public uploads by keyword to retrieve titles, descriptions, and tags. LlamaIndex parses these search results, embedding them into a temporary query index so your agent can cross-reference external content trends. This setup lets you build search agents that look at what is currently trending on Dailymotion. The agent extracts semantic patterns from the search results to help you plan your next video release.

Map account performance using an MCP Server

The `get_user` tool fetches your username, email, total videos, and overall views. Your LlamaIndex agent records these metrics over time, building a historical index of your channel growth. By coupling this tool with `list_channels`, the agent maps out how your content distributes across different Dailymotion categories. You get an updated knowledge base of your video distribution without writing custom API scrapers.

Setup guide

Set up Dailymotion 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 Dailymotion 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 Dailymotion tools.",
)
response = await agent.run("List recent Dailymotion 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 Dailymotion. 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 Dailymotion MCP in LlamaIndex

Install the tool package via `pip install llama-index-tools-mcp`. Initialize the client with `BasicMCPClient`, convert it using `McpToolSpec`, and pass the async tool list to your `FunctionAgent`.

Yes, the agent calls `list_playlists` to fetch playlist IDs and video counts, then feeds this data into your vector index. This allows your RAG application to answer queries about how your Dailymotion videos are grouped.

The agent uses `get_video` to pull live views and stream URLs directly from Dailymotion. By grounding its answers in this real-time tool output rather than static training data, the agent avoids generating incorrect statistics.

Yes, you can use the allowed_tools filter when setting up the client. If you only want the agent to read data, you can expose `list_videos` and block write tools like `delete_video` entirely.

Vinkius executes the MCP server within an isolated, ephemeral V8 sandbox. Your Dailymotion credentials and video stream URLs are processed in memory during the tool call and are never cached or written to persistent storage.

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