How to Use the TopOn / Mobvista MCP in LlamaIndex

Q: Does the MCP Server support querying Mintegral data with LlamaIndex?

The server offers multiple tools like getmintegralreporting and listmintegraloffers. These outputs are ideal for indexing, turning raw API calls into semantic knowledge.

Index ad performance history and query insights with LlamaIndex.

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Connect TopOn / Mobvista MCP to LlamaIndex

Create your Vinkius account to connect TopOn / Mobvista 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 TopOn / Mobvista with LlamaIndex

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Perplexity

Creating Searchable Ad Insights

Instead of just running a tool, you index the results. For example, after calling `get_mintegral_app_stats`, that output gets stored in your vector store. Later, you can ask LlamaIndex to query 'What were Mintegral stats for App X last month?' and get an answer grounded in actual API data.

RAG on Monetization Data

The `get_monetization_summary` tool output becomes part of a unified knowledge base. This lets you query past sessions or configurations and receive answers about overall monetization without needing to rerun the exact report.

Querying Ad Placements History

You can index all your ad placements using `list_topon_placements`. Then, LlamaIndex lets you ask semantic questions like 'Show me placement data for high-traffic games,' and it pulls the relevant, indexed details from the MCP Server.

Setup guide

Set up TopOn / Mobvista 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 TopOn / Mobvista 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 TopOn / Mobvista tools.",
)
response = await agent.run("List recent TopOn / Mobvista 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 TopOn / Mobvista. 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 TopOn / Mobvista MCP in LlamaIndex

LlamaIndex indexes the output of the MCP tools. This means every ad report or statistic, like results from `get_topon_reporting`, becomes searchable knowledge for your agent.

Yes. By indexing historical outputs—for instance, old waterfall reports from `get_topon_waterfall`—your RAG application can recall specific data points years later.

It provides performance metrics covering app stats, monetization summaries, and ad placements. These are all structured to be ingested into a queryable index.

The server offers multiple tools like `get_mintegral_reporting` and `list_mintegral_offers`. These outputs are ideal for indexing, turning raw API calls into semantic knowledge.

The server touches ad performance metrics, including eCPM, app statistics, and placement details. Indexing this data requires careful management of your vector store.

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