How to Use the Gumlet MCP in LangChain

Q: How do I connect the Gumlet MCP Server to a LangChain agent?

Install langchain-mcp-adapters and use the MultiServerMCPClient to connect to the server URL. From there, extract the tools and pass them directly to your ReAct agent setup.

Q: Can I trace Gumlet tool execution in LangChain using LangSmith?

Yes, every tool call like createvideoupload or getvideoanalytics gets logged automatically. You inspect the exact payload, latency, and token cost of each media operation inside your dashboard.

Run multi-step media pipelines in LangChain by chaining Gumlet asset uploads, optimization checks, and CDN analytics.

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Chain video processing with LangChain agents

The `create_video_upload` tool initiates a new video upload directly within your LangChain agent's execution chain. When your agent finishes uploading, it automatically hands off the output to `update_video_thumbnail` to set the exact visual frame for the player. This multi-step execution turns raw media into ready-to-stream assets without manual intervention. Your agent monitors the progress using `get_video_details` and decides when to trigger downstream publishing steps based on the output.

Track CDN performance through LangSmith

The `get_video_analytics` tool fetches raw performance metrics for your video assets directly into your active chain. By running this tool inside a LangChain agent, you trace every API response and latency metric through LangSmith to keep tabs on CDN delivery speeds. You get direct visibility into how your agent processes media performance data. If analytics show low engagement, the agent instantly queries `list_video_collections` to find alternative assets or reorganize your media library.

Keep your LangChain MCP Server integrations in sync

The `list_webhooks` tool exposes active event listeners to your LangChain agent, allowing it to verify system triggers during complex runs. Your agent runs this check alongside other API tools to ensure your media pipelines never lose track of active video transcodes. Connecting this MCP Server to your chains gives your agent access to image-heavy pages via `list_image_sources`. You combine live CDN data with databases, vector stores, and external APIs in a single cohesive execution loop.

Setup guide

Set up Gumlet MCP in LangChain

Prerequisites

Python 3.10+ installed
langchain-mcp-adapters + langgraph packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install langchain-mcp-adapters langgraph langchain-openai. The MCP adapters package converts MCP tools into native LangChain BaseTool objects.
2

Connect via HTTP transport
Use MultiServerMCPClient with "transport": "http" pointing to your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Create a ReAct agent
Pass the discovered tools to create_react_agent() from LangGraph. The agent automatically routes Gumlet tool calls through the MCP protocol.
4

Run with any LLM
Swap ChatOpenAI for ChatAnthropic, ChatGoogleGenerativeAI, or any LangChain-compatible model. The MCP tools work identically across all providers.

agent.py

from langchain_mcp_adapters.client import MultiServerMCPClient
from langgraph.prebuilt import create_react_agent
from langchain_openai import ChatOpenAI

async with MultiServerMCPClient({
    "gumlet-mcp": {
        "transport": "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,
    )
    result = await agent.ainvoke({
        "messages": "List recent Gumlet transactions"
    })
    print(result["messages"][-1].content)

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Common questions about Gumlet MCP in LangChain

Install `langchain-mcp-adapters` and use the `MultiServerMCPClient` to connect to the server URL. From there, extract the tools and pass them directly to your ReAct agent setup.

Yes, every tool call like `create_video_upload` or `get_video_analytics` gets logged automatically. You inspect the exact payload, latency, and token cost of each media operation inside your dashboard.

It treats each tool as a node in a chain. Your agent uses the output of `create_collection` as the immediate input for `create_video_upload`, managing the entire media lifecycle programmatically.

It removes the need to write boilerplate integration code. Your agent dynamically decides which tool to call based on the context of the conversation.

Your Gumlet video telemetry and account metadata are processed inside a zero-trust V8 isolate sandbox. No API keys or media statistics are written to persistent storage, keeping your CDN usage data isolated and ephemeral.

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