How to Use the AppFollow MCP in LlamaIndex

Index AppFollow store data into your LlamaIndex knowledge base for grounded, queryable answers.

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Connect AppFollow MCP to LlamaIndex

Create your Vinkius account to connect AppFollow 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 AppFollow with LlamaIndex

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Ground LlamaIndex answers in real store data

Instead of relying on base knowledge, turn `get_reviews_summary` output into indexed vector nodes. Your LlamaIndex agent pulls from this live knowledge base to answer user questions. Stop guessing about sentiment. By querying the index, your agent retrieves facts derived directly from recent store reviews and ratings.

Index historical ratings for semantic search

Fetch data via `get_ratings_history` and store the results in your vector store. LlamaIndex then allows you to search across time-series data using natural language. Ask questions about performance dips during specific months. The agent finds the relevant data points in the index and explains the trends based on the actual figures.

Filter AppFollow tool access for RAG

Control which data sources your agent can access by using the allowed_tools filter. You limit the agent to specific functions like `get_app_info` to keep your index clean. Only relevant store information enters your RAG pipeline. This prevents noise and ensures your agent stays focused on the data that matters for your specific project.

Setup guide

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

You use the MCP client to fetch tools and register them with the FunctionAgent. The agent then treats these tools as data providers for your indexed knowledge base.

Yes, you convert tool outputs into indexable documents. This allows your agent to query historical review data as part of its retrieval process.

Once indexed, your agent searches the data semantically. You get answers grounded in your specific store metrics rather than generic AI responses.

You manage the session lifecycle through the BasicMCPClient. The connection stays active while the agent performs its retrieval and indexing tasks.

The server transmits only the requested review and rating data to your local index. Your credentials are used only for the initial handshake and are not cached in the index itself.

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