How to Use the EX.CO Video Experience MCP in LangChain

Q: How does the agent handle custom playlists?

Your agent calls listvideoplaylists to fetch your curated video groups. It then parses the playlist IDs to run targeted performance checks on specific content sets.

Let your LangChain agents run multi-step video audits and fetch EX.CO performance metrics directly through structured chains.

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Connect EX.CO Video Experience MCP to LangChain

Create your Vinkius account to connect EX.CO Video Experience to LangChain and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup EX.CO Video Experience with LangChain

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Run automated video performance audits in LangChain

Your LangChain agent starts by calling `quick_video_performance_audit` to isolate low-performing assets before passing those IDs to deeper analytical chains. This tool-calling process feeds directly into your decision pipelines, letting the model check high-level metrics without manual intervention. The agent then uses `get_video_detailed_data` to extract the exact configuration issues causing the drop in engagement. Because LangChain tracks every step in LangSmith, you see the exact inputs and outputs of these EX.CO API calls in real time.

Map interactive content paths with LangChain chains

The `list_interactive_content` tool gives your LangChain agent immediate access to all quizzes and polls configured in your EX.CO account. The agent pulls this list and matches it against current video distributions to find alignment gaps. By feeding this output into `list_video_distribution_channels`, the agent determines which platforms lack interactive elements. You build a LangChain feedback loop where the model identifies missing engagement touchpoints and suggests distribution fixes.

Audit published assets using this MCP Server

This MCP Server exposes `list_successfully_published_videos` to let your LangChain agent verify active media assets against your internal database. The agent compares the published list with your records to find synchronization errors. Next, the agent calls `get_content_detailed_intelligence` to pull performance metrics for those verified videos. You get a clean, chain-linked audit of active EX.CO content without writing custom integration code.

Setup guide

Set up EX.CO Video Experience 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 EX.CO Video Experience 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({
    "exco-video-experience-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 EX.CO Video Experience transactions"
    })
    print(result["messages"][-1].content)

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by EX.CO Video Experience. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about EX.CO Video Experience MCP in LangChain

LangChain monitors token usage and latency for every tool call via LangSmith tracing. When your agent calls `get_video_analytics_summary`, the payload size and token count are logged automatically. This keeps your API usage transparent and budget-friendly.

You construct chains where the output of one tool feeds the next. For example, your agent runs `list_video_library` to find video IDs, then passes those IDs directly to `get_video_detailed_data` in the next step of the chain.

You initialize the connection using the MCP client adapter with the Vinkius endpoint URL. Then, pass the tools returned by the client to your LangChain agent constructor to make them instantly available.

Your agent calls `list_video_playlists` to fetch your curated video groups. It then parses the playlist IDs to run targeted performance checks on specific content sets.

Your analytics summaries and video metadata never persist on Vinkius servers. The platform acts as a zero-trust gateway, passing the API response directly to your local LangChain execution environment. All transport layers use TLS encryption to protect your proprietary engagement metrics.

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