How to Use the AppGallery Connect MCP in LlamaIndex

Q: Can I filter AppGallery Connect tools in LlamaIndex?

Yes, use the allowedtools filter when defining your agent. This limits the agent to only the specific data points you want to index.

Index your AppGallery Connect data into LlamaIndex to build RAG apps that answer questions about your app performance.

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Connect AppGallery Connect MCP to LlamaIndex

Create your Vinkius account to connect AppGallery Connect 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 AppGallery Connect with LlamaIndex

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RAG-ready AppGallery Connect data for LlamaIndex

The server fetches raw stats via `get_download_stats` and `get_install_stats`. LlamaIndex then indexes these figures into a vector store for semantic retrieval. Your RAG application answers questions like why a specific build saw a drop in installs. It queries the actual API data instead of guessing.

Building knowledge bases with LlamaIndex

Use `get_comments` to pull user feedback into your index. You can search across months of user sentiment to find patterns in your app's reception. This turns static review data into a queryable asset. Your agent retrieves specific user concerns to ground its responses in reality.

Contextual app management in LlamaIndex

The tool `get_app_info` provides metadata that LlamaIndex uses to categorize your index entries. This keeps your RAG pipeline organized by application ID. You avoid hallucination by forcing the agent to fetch current app status before generating a report. The index stays fresh with every query.

Setup guide

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

The server converts API responses into text chunks. LlamaIndex then processes these chunks into a vector index for your agent.

Yes, use the allowed_tools filter when defining your agent. This limits the agent to only the specific data points you want to index.

Your reviews are stored in a local, volatile vector index. We strip all sensitive account identifiers before the data hits your local store.

Install the llama-index-tools-mcp package and initialize a BasicMCPClient. Convert the tools to a list and pass them to your FunctionAgent.

Metadata is treated as context for your index. It is encrypted at the storage level and purged as soon as the agent session ends.

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