ContentGroove MCP Server for LlamaIndex 8 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add ContentGroove as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
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Vinkius supports streamable HTTP and SSE.
import asyncio
from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI
async def main():
# Your Vinkius token. get it at cloud.vinkius.com
mcp_client = BasicMCPClient("https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
mcp_tool_spec = McpToolSpec(client=mcp_client)
tools = await mcp_tool_spec.to_tool_list_async()
agent = FunctionAgent(
tools=tools,
llm=OpenAI(model="gpt-4o"),
system_prompt=(
"You are an assistant with access to ContentGroove. "
"You have 8 tools available."
),
)
response = await agent.run(
"What tools are available in ContentGroove?"
)
print(response)
asyncio.run(main())
* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure
About ContentGroove MCP Server
Integrate ContentGroove, an intelligent video processing engine, directly into your conversational workflow. Automate the process of finding the best highlights from massive podcasts. Use conversational text to command your AI to slice, transcribe, and pull highly engaging snippets from long-form videos.
LlamaIndex agents combine ContentGroove tool responses with indexed documents for comprehensive, grounded answers. Connect 8 tools through Vinkius and query live data alongside vector stores and SQL databases in a single turn. ideal for hybrid search, data enrichment, and analytical workflows.
What you can do
- Project Management — Instruct your AI to list tracked video projects to verify rendering status.
- Automated Video Splicing — Request the bot to target a large video, locate engaging discussions, and divide them into independent bite-sized clips natively.
- Metadata Extraction — Extract logically synced auto-transcribed subtitles alongside newly generated assets directly into your chat workspace.
The ContentGroove MCP Server exposes 8 tools through the Vinkius. Connect it to LlamaIndex in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.
How to Connect ContentGroove to LlamaIndex via MCP
Follow these steps to integrate the ContentGroove MCP Server with LlamaIndex.
Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token
Run the agent
Save to agent.py and run: python agent.py
Explore tools
The agent discovers 8 tools from ContentGroove
Why Use LlamaIndex with the ContentGroove MCP Server
LlamaIndex provides unique advantages when paired with ContentGroove through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine ContentGroove tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain ContentGroove tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query ContentGroove, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what ContentGroove tools were called, what data was returned, and how it influenced the final answer
ContentGroove + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the ContentGroove MCP Server delivers measurable value.
Hybrid search: combine ContentGroove real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query ContentGroove to augment indexed data with live information before generating user-facing responses
Knowledge base agents: build agents that maintain and update knowledge bases by periodically querying ContentGroove for fresh data
Analytical workflows: chain ContentGroove queries with LlamaIndex's data connectors to build multi-source analytical reports
ContentGroove MCP Tools for LlamaIndex (8)
These 8 tools become available when you connect ContentGroove to LlamaIndex via MCP:
create_direct_upload
Generate a signed URL for direct video upload
create_media_from_url
Import a video from a URL to generate AI highlights
get_clip_details
Get details of a specific highlight clip
get_media_clips
List all clips for a specific video
get_media_details
Get details of a specific media project
get_media_status
Check processing status of a media
list_all_clips
List all AI-generated clips
list_media
List all media projects in ContentGroove
Example Prompts for ContentGroove in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with ContentGroove immediately.
"Extract the 5 most engaging viral slices from project 'vid9x3a' for a social media campaign."
"Check the status of my latest video project render queue."
"List all recent AI-generated clips across my account."
Troubleshooting ContentGroove MCP Server with LlamaIndex
Common issues when connecting ContentGroove to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpContentGroove + LlamaIndex FAQ
Common questions about integrating ContentGroove MCP Server with LlamaIndex.
How does LlamaIndex connect to MCP servers?
Can I combine MCP tools with vector stores?
Does LlamaIndex support async MCP calls?
Connect ContentGroove with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
Anthropic's native desktop app for Claude with built-in MCP support.
AI-first code editor with integrated LLM-powered coding assistance.
GitHub Copilot in VS Code with Agent mode and MCP support.
Purpose-built IDE for agentic AI coding workflows.
Autonomous AI coding agent that runs inside VS Code.
Anthropic's agentic CLI for terminal-first development.
Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
Type-safe agent development for Python with first-class MCP support.
TypeScript toolkit for building AI-powered web applications.
TypeScript-native agent framework for modern web stacks.
Python framework for orchestrating collaborative AI agent crews.
Leading Python framework for composable LLM applications.
Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
Connect ContentGroove to LlamaIndex
Get your token, paste the configuration, and start using 8 tools in under 2 minutes. No API key management needed.