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Tubular MCP Server for LangChainGive LangChain instant access to 12 tools to Check Api Health, Get Api Rate Limits, Get Audience Overlap, and more

Built by Vinkius GDPR 12 Tools Framework

LangChain is the leading Python framework for composable LLM applications. Connect Tubular 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 Tubular app connector for LangChain is a standout in the Data Analytics 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

python
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({
        "tubular": {
            "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 Tubular, show me what tools are available.",
            }]
        })
        print(response["messages"][-1].content)

asyncio.run(main())
Tubular
Fully ManagedVinkius Servers
60%Token savings
High SecurityEnterprise-grade
IAMAccess control
EU AI ActCompliant
DLPData protection
V8 IsolateSandboxed
Ed25519Audit chain
<40msKill switch
Stream every event to Splunk, Datadog, or your own webhook in real-time

* 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 Tubular MCP Server

Connect your Tubular Labs video intelligence account to any AI agent and simplify how you analyze digital video trends, creator performance, and cross-platform audience metrics through natural conversation.

LangChain's ecosystem of 500+ components combines seamlessly with Tubular 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 Insights — Retrieve detailed performance metrics and trending data for individual videos or categories across social platforms.
  • Creator Intelligence — Search for creators and fetch high-level performance summaries, trends, and audience ratings.
  • Audience Demographics — Analyze audience breakdowns (age, gender, location) for specific videos or creators to refine your targeting.
  • Sponsorship Tracking — List brand sponsors and monitor sponsored video campaigns to understand the competitive landscape.
  • Audience Overlap — Analyze shared audience between two creators or content properties to identify partnership opportunities.
  • Operational Monitoring — Check API health and rate limits to ensure your intelligence engine is always running.

The Tubular 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 Tubular tools available for LangChain

When LangChain connects to Tubular through Vinkius, your AI agent gets direct access to every tool listed below — spanning video-intelligence, audience-insights, competitive-benchmarking, 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.

check_api_health

Check API health status

get_api_rate_limits

Get current API rate limits

get_audience_overlap

Analyze shared audience between entities

get_audience_ratings

Get reach and engagement ratings

get_creator_summary

Get summary for a specific creator

get_creator_trends

Get trends for a specific creator

get_video_demographics

) for a specific video. Get audience demographics for a video

get_video_insights

Get insights for a specific video

get_video_trends

List trending videos

list_sponsored_campaigns

List sponsored video campaigns

list_sponsors

List sponsors and brand partners

search_creators

g., name or keywords). Search for creators

Connect Tubular to LangChain via MCP

Follow these steps to wire Tubular into LangChain. The entire setup takes under two minutes — your credentials stay safe behind the Vinkius.

01

Install dependencies

Run pip install langchain langchain-mcp-adapters langgraph langchain-openai
02

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token
03

Run the agent

Save the code and run python agent.py
04

Explore tools

The agent discovers 12 tools from Tubular via MCP

Why Use LangChain with the Tubular MCP Server

LangChain provides unique advantages when paired with Tubular through the Model Context Protocol.

01

The largest ecosystem of integrations, chains, and agents. combine Tubular MCP tools with 500+ LangChain components

02

Agent architecture supports ReAct, Plan-and-Execute, and custom strategies with full MCP tool access at every step

03

LangSmith tracing gives you complete visibility into tool calls, latencies, and token usage for production debugging

04

Memory and conversation persistence let agents maintain context across Tubular queries for multi-turn workflows

Tubular + LangChain Use Cases

Practical scenarios where LangChain combined with the Tubular MCP Server delivers measurable value.

01

RAG with live data: combine Tubular tool results with vector store retrievals for answers grounded in both real-time and historical data

02

Autonomous research agents: LangChain agents query Tubular, synthesize findings, and generate comprehensive research reports

03

Multi-tool orchestration: chain Tubular tools with web scrapers, databases, and calculators in a single agent run

04

Production monitoring: use LangSmith to trace every Tubular tool call, measure latency, and optimize your agent's performance

Example Prompts for Tubular in LangChain

Ready-to-use prompts you can give your LangChain agent to start working with Tubular immediately.

01

"Search for top gaming creators in my region."

02

"Show me the audience overlap between creator 'A-101' and 'B-552'."

03

"List all active sponsored video campaigns from 'TechBrand Inc'."

Troubleshooting Tubular MCP Server with LangChain

Common issues when connecting Tubular to LangChain through the Vinkius, and how to resolve them.

01

MultiServerMCPClient not found

Install: pip install langchain-mcp-adapters

Tubular + LangChain FAQ

Common questions about integrating Tubular MCP Server with LangChain.

01

How does LangChain connect to MCP servers?

Use langchain-mcp-adapters to create an MCP client. LangChain discovers all tools and wraps them as native LangChain tools compatible with any agent type.
02

Which LangChain agent types work with MCP?

All agent types including ReAct, OpenAI Functions, and custom agents work with MCP tools. The tools appear as standard LangChain tools after the adapter wraps them.
03

Can I trace MCP tool calls in LangSmith?

Yes. All MCP tool invocations appear as traced steps in LangSmith, showing input parameters, response payloads, latency, and token usage.