AppFollow MCP Server for LlamaIndex 8 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add AppFollow as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
ASK AI ABOUT THIS MCP SERVER
Vinkius supports streamable HTTP and SSE.
import asyncio
from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI
async def main():
# Your Vinkius token. get it at cloud.vinkius.com
mcp_client = BasicMCPClient("https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
mcp_tool_spec = McpToolSpec(client=mcp_client)
tools = await mcp_tool_spec.to_tool_list_async()
agent = FunctionAgent(
tools=tools,
llm=OpenAI(model="gpt-4o"),
system_prompt=(
"You are an assistant with access to AppFollow. "
"You have 8 tools available."
),
)
response = await agent.run(
"What tools are available in AppFollow?"
)
print(response)
asyncio.run(main())
* 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 AppFollow MCP Server
The AppFollow MCP Server brings powerful app store intelligence directly to your AI agent. Monitor your app's reputation, track your position in the charts, and analyze user feedback across all major app stores with ease.
LlamaIndex agents combine AppFollow tool responses with indexed documents for comprehensive, grounded answers. Connect 8 tools through Vinkius and query live data alongside vector stores and SQL databases in a single turn. ideal for hybrid search, data enrichment, and analytical workflows.
Key Features
- Review Management — List and search for user reviews across different countries and languages.
- AI & Sentiment Analysis — Get AI-generated summaries of user feedback and analyze the overall sentiment of your reviews.
- Ranking Tracker — Monitor your app's performance in store charts and track daily changes in visibility.
- Rating Metrics — Access current star rating distributions and historical rating trends over time.
- App Information — Retrieve detailed metadata and store information for any app on the market.
- Competitive Benchmarking — Compare your app's performance against competitors using global store data.
Benefits for Teams
- Customer Support — Quickly identify common user issues and bugs reported in reviews.
- Product Managers — Use AI summaries to understand user sentiment and prioritize feature requests.
- Growth & Marketing — Track rankings and ratings to measure the effectiveness of your ASO and UA efforts.
The AppFollow MCP Server exposes 8 tools through the Vinkius. Connect it to LlamaIndex in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.
How to Connect AppFollow to LlamaIndex via MCP
Follow these steps to integrate the AppFollow MCP Server with LlamaIndex.
Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token
Run the agent
Save to agent.py and run: python agent.py
Explore tools
The agent discovers 8 tools from AppFollow
Why Use LlamaIndex with the AppFollow MCP Server
LlamaIndex provides unique advantages when paired with AppFollow through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine AppFollow tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain AppFollow tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query AppFollow, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what AppFollow tools were called, what data was returned, and how it influenced the final answer
AppFollow + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the AppFollow MCP Server delivers measurable value.
Hybrid search: combine AppFollow real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query AppFollow to augment indexed data with live information before generating user-facing responses
Knowledge base agents: build agents that maintain and update knowledge bases by periodically querying AppFollow for fresh data
Analytical workflows: chain AppFollow queries with LlamaIndex's data connectors to build multi-source analytical reports
AppFollow MCP Tools for LlamaIndex (8)
These 8 tools become available when you connect AppFollow to LlamaIndex via MCP:
get_account_check
Verify AppFollow account connection
get_app_info
Retrieve basic information about an app from AppFollow
get_rankings
Track app rankings in store charts
get_ratings
Get current star rating distribution
get_ratings_history
Get historical rating data over a period of time
get_reviews_ai_summary
Get an AI-generated summary of recent user reviews
get_reviews_summary
Get a summary of reviews and average rating
list_reviews
List app reviews for a specific app store product
Example Prompts for AppFollow in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with AppFollow immediately.
"What are the most recent 1-star reviews for my app?"
"Give me an AI summary of user feedback for 'com.example.app'."
"Where does my app rank in the 'Health & Fitness' category in the US today?"
Troubleshooting AppFollow MCP Server with LlamaIndex
Common issues when connecting AppFollow to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpAppFollow + LlamaIndex FAQ
Common questions about integrating AppFollow MCP Server with LlamaIndex.
How does LlamaIndex connect to MCP servers?
Can I combine MCP tools with vector stores?
Does LlamaIndex support async MCP calls?
Connect AppFollow with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
Anthropic's native desktop app for Claude with built-in MCP support.
AI-first code editor with integrated LLM-powered coding assistance.
GitHub Copilot in VS Code with Agent mode and MCP support.
Purpose-built IDE for agentic AI coding workflows.
Autonomous AI coding agent that runs inside VS Code.
Anthropic's agentic CLI for terminal-first development.
Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
Type-safe agent development for Python with first-class MCP support.
TypeScript toolkit for building AI-powered web applications.
TypeScript-native agent framework for modern web stacks.
Python framework for orchestrating collaborative AI agent crews.
Leading Python framework for composable LLM applications.
Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
Connect AppFollow to LlamaIndex
Get your token, paste the configuration, and start using 8 tools in under 2 minutes. No API key management needed.