How to Use the Tubular MCP in LlamaIndex

Q: How do I verify Tubular functionality with LlamaIndex?

You use getapiratelimits to check the usage capacity. The MCP Server returns your current limits and how quickly you're approaching them, which is crucial for planning your indexing jobs.

Q: Can LlamaIndex analyze multiple video trends from Tubular?

Yes, after calling getvideotrends, the results are indexed. You can then run follow-up queries that combine current trending data with historical knowledge stored in your vector database.

Q: What is the best way to use Tubular and LlamaIndex together?

You first call a tool like searchcreators to gather fresh data. Then, you index that output alongside your internal documents using LlamaIndex's RAG capabilities. This grounds your AI client in verifiable facts.

Index social performance data into your knowledge base using LlamaIndex.

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Connect Tubular MCP to LlamaIndex

Create your Vinkius account to connect Tubular to LlamaIndex 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 LlamaIndex

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Indexing Creator Profiles with LlamaIndex

When you run `get_creator_summary`, the MCP Server output isn't just used in a query—it indexes the creator's core metrics into your vector store. This means you can query past sessions and get grounded answers about that creator years later. The developer builds RAG applications where these live API data points combine with documents, giving you historical context for 'How did Creator X perform last quarter?'

Building Searchable Campaign Knowledge with LlamaIndex

Using `list_sponsored_campaigns` populates your knowledge base with structured records of every campaign. This indexed data allows you to query not just 'what campaigns ran,' but 'which types of campaigns typically run in Q3?' The MCP Server ensures that the results, like a list of brand partners from `list_sponsors`, become searchable facts within your index, reducing hallucinations.

Analyzing Audience Overlap for LlamaIndex

The `get_audience_overlap` tool provides specific overlap percentages. When indexed, these metrics create highly precise knowledge chunks that you can retrieve later. Your application doesn't just see a number; it sees the context of why two groups overlap. This persistent data structure lets you query historical relationships: 'What was the audience overlap between Brand A and Creator B during the 2023 holiday period?'

Setup guide

Set up Tubular MCP in LlamaIndex

Prerequisites

Python 3.10+ installed
llama-index-tools-mcp package
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai. The MCP tools package provides BasicMCPClient and McpToolSpec.
2

Connect with BasicMCPClient
Point BasicMCPClient to your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports SSE and Streamable HTTP transports.
3

Convert to LlamaIndex tools
Call mcp_tool_spec.to_tool_list_async() to convert all Tubular MCP tools into native FunctionTool objects that any LlamaIndex agent can use.
4

Run with any LLM
Create a FunctionAgent with the tools and your preferred LLM. Swap OpenAI for Anthropic, Gemini, or any LlamaIndex-supported provider.

agent.py

from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

# Connect to the MCP
mcp_client = BasicMCPClient(
    "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)
mcp_tool_spec = McpToolSpec(client=mcp_client)

# Convert MCP tools to LlamaIndex tools
tools = await mcp_tool_spec.to_tool_list_async()

# Create and run the agent
agent = FunctionAgent(
    tools=tools,
    llm=OpenAI(model="gpt-4o"),
    system_prompt="You have access to Tubular tools.",
)
response = await agent.run("List recent Tubular data")

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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 LlamaIndex

You use `get_api_rate_limits` to check the usage capacity. The MCP Server returns your current limits and how quickly you're approaching them, which is crucial for planning your indexing jobs.

Yes, after calling `get_video_trends`, the results are indexed. You can then run follow-up queries that combine current trending data with historical knowledge stored in your vector database.

You first call a tool like `search_creators` to gather fresh data. Then, you index that output alongside your internal documents using LlamaIndex's RAG capabilities. This grounds your AI client in verifiable facts.

The server works with structured video and creator data. When you call `get_video_demographics`, the resulting demographic breakdowns are what get indexed as searchable knowledge, making them highly useful.

This server handles aggregated audience metrics and video identifiers. The primary sensitive data type accessed is demographic information (age ranges, gender) for the videos you analyze.

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