How to Use the Digital Turbine MCP in LlamaIndex

Index your Digital Turbine campaign data into LlamaIndex to build a knowledge base that answers growth questions.

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Connect Digital Turbine MCP to LlamaIndex

Create your Vinkius account to connect Digital Turbine 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 Digital Turbine with LlamaIndex

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Index campaign analytics for semantic search

Your LlamaIndex agent calls `get_campaign_analytics` and stores the output in your vector store. You can now query your historical performance data using natural language instead of raw IDs. This turns your API results into a searchable history. Your agent understands context from past campaigns to inform future growth decisions.

Ground AI decisions in live API data

By connecting `list_top_performing_apps` to your knowledge base, your agent stops guessing about which apps earn the most. It retrieves the latest figures directly from the source to keep its reasoning grounded. This prevents the common issue of hallucinated metrics. The AI answers questions based on the actual state of your Digital Turbine account.

Unified access to platform metadata

Use `get_platform_metadata` to keep your agent updated on account limits and configurations. This data is indexed alongside your documents, allowing the agent to check constraints before suggesting changes. Your RAG application stays aware of the environment. It knows exactly what it can and cannot do based on your live account settings.

Setup guide

Set up Digital Turbine 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 Digital Turbine 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 Digital Turbine tools.",
)
response = await agent.run("List recent Digital Turbine data")

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Common questions about Digital Turbine MCP in LlamaIndex

You use the MCP tool spec to fetch the tool definitions and add them to your FunctionAgent. The agent then performs the retrieval, and you index those results into your vector database.

Yes, by converting the JSON output of the tools into a document format, your LlamaIndex setup can index and search the content. This makes your API data part of your long-term knowledge.

The MCP server ensures that only the data you explicitly query is retrieved. Your private app configurations and revenue logs never leave the secure, ephemeral connection environment.

Yes, after you connect the server, your agent can issue natural language queries that result in tool calls. It retrieves the data, and you get an answer based on the real-time API response.

Your vector store handles the persistence of the data you choose to index. If you need fresh data, you simply instruct your agent to call the tools again to update the knowledge base.

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