Gumlet MCP Server for LangChainGive LangChain instant access to 12 tools to Create Collection, Create Video Upload, Delete Video, and more
LangChain is the leading Python framework for composable LLM applications. Connect Gumlet through Vinkius and LangChain agents can call every tool natively. combine them with retrievers, memory, and output parsers for sophisticated AI pipelines.
Ask AI about this App Connector for LangChain
The Gumlet app connector for LangChain 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 langchain_mcp_adapters.client import MultiServerMCPClient
from langchain_openai import ChatOpenAI
from langgraph.prebuilt import create_react_agent
async def main():
# Your Vinkius token. get it at cloud.vinkius.com
async with MultiServerMCPClient({
"gumlet": {
"transport": "streamable_http",
"url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
}
}) as client:
tools = client.get_tools()
agent = create_react_agent(
ChatOpenAI(model="gpt-4o"),
tools,
)
response = await agent.ainvoke({
"messages": [{
"role": "user",
"content": "Using Gumlet, show me what tools are available.",
}]
})
print(response["messages"][-1].content)
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.
LangChain's ecosystem of 500+ components combines seamlessly with Gumlet through native MCP adapters. Connect 12 tools via Vinkius and use ReAct agents, Plan-and-Execute strategies, or custom agent architectures. with LangSmith tracing giving full visibility into every tool call, latency, and token cost.
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 LangChain 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 LangChain
When LangChain 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 LangChain via MCP
Follow these steps to wire Gumlet into LangChain. The entire setup takes under two minutes — your credentials stay safe behind the Vinkius.
Install dependencies
pip install langchain langchain-mcp-adapters langgraph langchain-openaiReplace the token
[YOUR_TOKEN_HERE] with your Vinkius tokenRun the agent
python agent.pyExplore tools
Why Use LangChain with the Gumlet MCP Server
LangChain provides unique advantages when paired with Gumlet through the Model Context Protocol.
The largest ecosystem of integrations, chains, and agents. combine Gumlet MCP tools with 500+ LangChain components
Agent architecture supports ReAct, Plan-and-Execute, and custom strategies with full MCP tool access at every step
LangSmith tracing gives you complete visibility into tool calls, latencies, and token usage for production debugging
Memory and conversation persistence let agents maintain context across Gumlet queries for multi-turn workflows
Gumlet + LangChain Use Cases
Practical scenarios where LangChain combined with the Gumlet MCP Server delivers measurable value.
RAG with live data: combine Gumlet tool results with vector store retrievals for answers grounded in both real-time and historical data
Autonomous research agents: LangChain agents query Gumlet, synthesize findings, and generate comprehensive research reports
Multi-tool orchestration: chain Gumlet tools with web scrapers, databases, and calculators in a single agent run
Production monitoring: use LangSmith to trace every Gumlet tool call, measure latency, and optimize your agent's performance
Example Prompts for Gumlet in LangChain
Ready-to-use prompts you can give your LangChain 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 LangChain
Common issues when connecting Gumlet to LangChain through the Vinkius, and how to resolve them.
MultiServerMCPClient not found
pip install langchain-mcp-adaptersGumlet + LangChain FAQ
Common questions about integrating Gumlet MCP Server with LangChain.
How does LangChain connect to MCP servers?
langchain-mcp-adapters to create an MCP client. LangChain discovers all tools and wraps them as native LangChain tools compatible with any agent type.