How to Use the Appbot MCP in LlamaIndex

Q: How do I index Appbot data using LlamaIndex?

Use llama-index-tools-mcp to initialize the McpToolSpec with your Vinkius endpoint. You can then call the tools to load app reviews directly into your document parser.

Q: Can LlamaIndex query Appbot reviews across different countries?

Yes, the agent can call listcountries to find valid region filters, then pass those filters to listreviews before indexing the localized feedback.

Q: How does the LlamaIndex agent know which Appbot tools to use?

You pass the tool list from McpToolSpec to a FunctionAgent. The agent inspects the tool schemas and decides whether to list reviews, check custom topics, or fetch specific review details.

Index app reviews and sentiment data into vector stores for semantic search using LlamaIndex.

See Vinkius in Action

Works with every AI agent you already use

…and any MCP-compatible client

MCP Servers - Free for Subscribers

Connect Appbot MCP to LlamaIndex

Create your Vinkius account to connect Appbot 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.

GDPR Free for Subscribers

Setup Appbot with LlamaIndex

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Build RAG engines over mobile app feedback

The `list_reviews` tool extracts raw text reviews, ratings, and metadata from your iOS and Android apps directly into your indexing pipeline via the MCP Server. LlamaIndex ingests this stream, converts the reviews into vector embeddings, and stores them in your database for semantic querying. Instead of searching by static keywords, you query your index for conceptual issues like login failures or payment friction. The system retrieves matching reviews even if users used different words to describe the same problem.

Ground agent responses in actual Appbot MCP Server data

The `get_review_details` tool provides the exact text and metadata of a specific customer review to ground your agent's responses. By fetching the source data directly, your LlamaIndex RAG application avoids hallucinating user complaints or review scores. Your query engine verifies the retrieved review details against `list_versions` to ensure the context matches the correct app release. This keeps your automated support reports accurate and factual.

Map custom topics to semantic search categories

The `list_custom_topics` tool retrieves the custom categories your team set up to track specific user behaviors. LlamaIndex uses these definitions to tag and organize incoming reviews during the vector indexing phase. Your index queries `get_reviews_by_custom_topic` to build a historical baseline of feedback for each custom tag. You can then run comparative semantic searches across different timeframes or app versions to see how sentiment evolves.

Setup guide

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

Get your connection token →

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Appbot. 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.

Why Choose Vinkius

Vinkius connects your tools to AI with real-time monitoring and automatic cost savings — all from one dashboard.

Connect Appbot now

Real-time monitoring

Live

visibility into every interaction

Connect your favorite tools to your AI and see exactly what's happening — every request, every response, in real time.

Built-in savings

60%

lower AI costs

Vinkius compresses data between your apps and your AI automatically. Lower bills every month — no configuration required.

Single dashboard

One

place for every integration

Every tool your AI connects to, managed from a single screen. One account, complete control.

Common questions about Appbot MCP in LlamaIndex

Use `llama-index-tools-mcp` to initialize the `McpToolSpec` with your Vinkius endpoint. You can then call the tools to load app reviews directly into your document parser.

Yes, the agent can call `list_countries` to find valid region filters, then pass those filters to `list_reviews` before indexing the localized feedback.

You pass the tool list from `McpToolSpec` to a `FunctionAgent`. The agent inspects the tool schemas and decides whether to list reviews, check custom topics, or fetch specific review details.

Yes, you can build a query pipeline that periodically checks `list_topics` for emerging issues. The system indexes these topics and flags new, repeating complaints to your engineering team.

Your app reviews, ratings, and custom topics are fetched via secure HTTPS connections managed by the Vinkius MCP platform. The data is processed in ephemeral memory and only stored in the vector databases you explicitly configure.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

OpenAI Agents SDK sdk-python

Google ADK sdk-python

Pydantic AI sdk-python

Vercel AI SDK sdk-typescript