SproutVideo MCP Server for LlamaIndexGive LlamaIndex instant access to 11 tools to Create Playlist, Delete Video, Get Account, and more
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add SproutVideo as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
Ask AI about this App Connector for LlamaIndex
The SproutVideo app connector for LlamaIndex is a standout in the Productivity category — giving your AI agent 11 tools to work with, ready to go from day one.
Vinkius delivers Streamable HTTP and SSE to any MCP client
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 SproutVideo. "
"You have 11 tools available."
),
)
response = await agent.run(
"What tools are available in SproutVideo?"
)
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 SproutVideo MCP Server
Connect your SproutVideo account to any AI agent and simplify your video hosting and content management through natural conversation.
LlamaIndex agents combine SproutVideo tool responses with indexed documents for comprehensive, grounded answers. Connect 11 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
- Video Management — List all hosted videos, retrieve detailed metadata, and monitor playback stats and plays
- Playlist Control — Query and manage video playlists to organize your content delivery
- Metadata Automation — Update video titles, descriptions, and tags programmatically directly from your agent
- Cleanup & Maintenance — Delete old or redundant videos and manage your storage usage
- Operational Insights — Query video tags to understand your library structure and content distribution
The SproutVideo MCP Server exposes 11 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.
All 11 SproutVideo tools available for LlamaIndex
When LlamaIndex connects to SproutVideo through Vinkius, your AI agent gets direct access to every tool listed below — spanning video-hosting, video-cms, streaming, and more. Every call is secured with network, filesystem, subprocess, and code evaluation entitlements inside a sandboxed runtime. Beyond a simple connection, you get a full AI Gateway with real-time visibility into agent activity, enterprise governance, and optimized token usage.
Create a new video playlist
Delete a video
Get account information and usage
Get details for a specific playlist
Get analytics for a specific video
Get details for a specific video
List all video folders
List video playlists
List video tags
List SproutVideo videos
Update video details
Connect SproutVideo to LlamaIndex via MCP
Follow these steps to wire SproutVideo into LlamaIndex. The entire setup takes under two minutes — your credentials stay safe behind the Vinkius.
Install dependencies
pip install llama-index-tools-mcp llama-index-llms-openaiReplace the token
[YOUR_TOKEN_HERE] with your Vinkius tokenRun the agent
agent.py and run: python agent.pyExplore tools
Why Use LlamaIndex with the SproutVideo MCP Server
LlamaIndex provides unique advantages when paired with SproutVideo through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine SproutVideo tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain SproutVideo tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query SproutVideo, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what SproutVideo tools were called, what data was returned, and how it influenced the final answer
SproutVideo + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the SproutVideo MCP Server delivers measurable value.
Hybrid search: combine SproutVideo real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query SproutVideo 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 SproutVideo for fresh data
Analytical workflows: chain SproutVideo queries with LlamaIndex's data connectors to build multi-source analytical reports
Example Prompts for SproutVideo in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with SproutVideo immediately.
"List all videos in SproutVideo."
"Show me the video engagement analytics for all published videos with viewer retention data."
"Create a new playlist called Product Tutorials and add the top 5 most viewed tutorial videos."
Troubleshooting SproutVideo MCP Server with LlamaIndex
Common issues when connecting SproutVideo to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpSproutVideo + LlamaIndex FAQ
Common questions about integrating SproutVideo MCP Server with LlamaIndex.