How to Use the ContentGroove MCP in LlamaIndex

Q: How do I index ContentGroove video metadata into a LlamaIndex vector store?

You query your catalog using listallclips and load the text payloads into LlamaIndex Document objects. From there, you can embed and store them in any supported vector database.

Q: Can LlamaIndex query the status of a video processing job in ContentGroove?

Yes, the agent can call getmediastatus to verify if a video is ready before attempting to index its clips. This prevents empty document errors in your index.

Q: How does the ContentGroove MCP Server help with semantic search over videos in LlamaIndex?

The server provides direct access to clip metadata through getclipdetails. LlamaIndex uses this data to map natural language queries to specific time-stamped video highlights.

Q: Can I filter which clips get indexed based on their details?

Yes, your LlamaIndex agent can use getmediaclips to fetch highlights for a specific project and filter them by duration or confidence score before indexing.

Index your ContentGroove video highlights directly into LlamaIndex vector stores using this MCP Server.

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Connect ContentGroove MCP to LlamaIndex

Create your Vinkius account to connect ContentGroove 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 ContentGroove with LlamaIndex

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Grounding RAG in Video Clip Metadata

Your LlamaIndex agent extracts all video highlights using `list_all_clips` to index them directly into a vector database. When users ask questions about your media, the agent queries the index and pulls the exact clip details via `get_clip_details`. This grounds your RAG pipeline in real video data rather than LLM hallucinations.

Semantic Search Across ContentGroove Media

Your LlamaIndex agent searches your catalog using `list_media` to map natural language queries to project IDs. By hooking this MCP Server up to your index, users can search for specific topics and get direct links to the relevant videos. The agent maps natural language queries to the actual media IDs retrieved from the API.

Automated Video Ingestion for LlamaIndex Pipelines

Your pipeline triggers video processing using `create_media_from_url` to feed new footage into your knowledge base. The agent monitors the processing via `get_media_status` and automatically indexes the newly generated clips once they are ready. It turns your video production pipeline into an autonomous knowledge-gathering loop.

Setup guide

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

You query your catalog using `list_all_clips` and load the text payloads into LlamaIndex Document objects. From there, you can embed and store them in any supported vector database.

Yes, the agent can call `get_media_status` to verify if a video is ready before attempting to index its clips. This prevents empty document errors in your index.

The server provides direct access to clip metadata through `get_clip_details`. LlamaIndex uses this data to map natural language queries to specific time-stamped video highlights.

Yes, your LlamaIndex agent can use `get_media_clips` to fetch highlights for a specific project and filter them by duration or confidence score before indexing.

Your video URLs and clip metadata are transferred securely over HTTPS. Vinkius runs the MCP connection in a zero-trust sandbox, so your credentials and indexing payloads are never cached or leaked.

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