Gumlet MCP Server for LlamaIndexGive LlamaIndex instant access to 12 tools to Create Collection, Create Video Upload, Delete Video, and more
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Gumlet 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 Gumlet app connector for LlamaIndex is a standout in the Image Video category — giving your AI agent 12 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 Gumlet. "
"You have 12 tools available."
),
)
response = await agent.run(
"What tools are available in Gumlet?"
)
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 Gumlet MCP Server
Connect your Gumlet account to any AI agent and take full control of your video hosting and image optimization workflows through natural conversation.
LlamaIndex agents combine Gumlet tool responses with indexed documents for comprehensive, grounded answers. Connect 12 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 Lifecycle — Manage the complete video lifecycle from creating new uploads and retrieving metadata to monitoring transcoding status
- Media Organization — Create and manage collections/folders programmatically to maintain a structured media library
- Visual Control — Automate thumbnail updates by selecting specific video frames or time offsets for perfect visual representation
- Optimization Insights — Monitor real-time video analytics, viewing metrics, and bandwidth usage for every asset in your account
- Image Source Management — List and manage image optimization sources and organization users to ensure high-fidelity delivery
The Gumlet MCP Server exposes 12 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 12 Gumlet tools available for LlamaIndex
When LlamaIndex connects to Gumlet through Vinkius, your AI agent gets direct access to every tool listed below — spanning video-hosting, image-optimization, cdn-delivery, 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.
Add new folder
Upload new video
Remove video asset
Get profile details
Check video stats
Check video status
List image optimized sources
List team members
List folders
List video assets
Get active webhooks
Set thumbnail offset
Connect Gumlet to LlamaIndex via MCP
Follow these steps to wire Gumlet 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 Gumlet MCP Server
LlamaIndex provides unique advantages when paired with Gumlet through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Gumlet tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Gumlet tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Gumlet, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Gumlet tools were called, what data was returned, and how it influenced the final answer
Gumlet + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Gumlet MCP Server delivers measurable value.
Hybrid search: combine Gumlet real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Gumlet 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 Gumlet for fresh data
Analytical workflows: chain Gumlet queries with LlamaIndex's data connectors to build multi-source analytical reports
Example Prompts for Gumlet in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Gumlet immediately.
"Create a new video upload in collection 'col_123' titled 'Annual Report 2026'."
"Check the transcoding status of video 'asset_987'."
"Show me the viewing stats for my latest product video."
Troubleshooting Gumlet MCP Server with LlamaIndex
Common issues when connecting Gumlet to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpGumlet + LlamaIndex FAQ
Common questions about integrating Gumlet MCP Server with LlamaIndex.