How to Use the Tubular MCP in LangChain

Q: How do I check if Tubular works with LangChain?

The checkapihealth tool confirms the entire MCP Server is up and running. You'll get an immediate status code, letting your agent know right away that it can proceed with analytics.

Q: Can I compare audiences using Tubular and LangChain?

Absolutely. The getaudienceoverlap tool is designed for this. You pass two distinct groups (like two different creators' followers), and the MCP Server returns exactly what percentage of their audience shares common ground.

Q: What if I need to see trending content with Tubular and LangChain?

You use getvideotrends to get a list of what's hot right now across the platforms. This tool is perfect for giving your agent initial ideas on where to focus its analysis or which creators to investigate first.

Build complex social analytics workflows with LangChain.

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Connect Tubular MCP to LangChain

Create your Vinkius account to connect Tubular 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 Tubular with LangChain

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ChatGPT

Claude

Perplexity

Multi-step Creator Analysis for LangChain

When you call `get_creator_summary`, your agent gets the core metrics needed to decide what's next. This output tells the system if it needs to run a trend analysis, so the agent can immediately invoke `get_creator_trends`. This sequence allows your ReAct agents to build deep reasoning pipelines. They don't just call tools; they use the intermediate result—like spotting a high engagement rating from `get_audience_ratings`—to decide which other MCP Server tool or database connection to hit next.

Sponsor Campaign Comparison with LangChain

To compare brand performance, you can run the tools that list all campaigns and partners. The agent first calls `list_sponsored_campaigns` to get a roster of active efforts. Next, it uses the names found there to call `list_sponsors`, mapping specific brands to their current activities. This chain lets your AI client systematically gather context before forming an answer. You're building a decision matrix: 'Which campaign is running for which sponsor?' All handled by chaining tool outputs.

Video Performance Benchmarking with LangChain

Need to know why one video performs better than another? Start by calling `get_video_insights` on a specific piece of content. That output provides the key performance indicators (KPIs) that feed directly into the next step. From those insights, your agent can determine if demographic data is needed and call `get_video_demographics`. This allows you to build sophisticated comparison logic—for example, comparing demographics for top-performing videos against average ones. The MCP Server handles the full pipeline.

Setup guide

Set up Tubular 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 Tubular 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({
    "tubular-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 Tubular 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 Tubular. 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 Tubular MCP in LangChain

The `check_api_health` tool confirms the entire MCP Server is up and running. You'll get an immediate status code, letting your agent know right away that it can proceed with analytics.

Absolutely. The `get_audience_overlap` tool is designed for this. You pass two distinct groups (like two different creators' followers), and the MCP Server returns exactly what percentage of their audience shares common ground.

Yes, all calls are governed by your single endpoint token. The server processes specific identifiers like creator names or video IDs; it never handles raw user credentials. This is standard practice for the MCP Server.

You use `get_video_trends` to get a list of what's hot right now across the platforms. This tool is perfect for giving your agent initial ideas on where to focus its analysis or which creators to investigate first.

This server primarily works with aggregated audience metrics and video identifiers, specifically accessing demographic data (age ranges, genders) for the videos you analyze.

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