How to Use the Appcues MCP in LlamaIndex

Index your Appcues onboarding flows and user segments directly into LlamaIndex for semantic search.

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Connect Appcues MCP to LlamaIndex

Create your Vinkius account to connect Appcues 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 Appcues with LlamaIndex

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Index user segments into LlamaIndex

Querying your audience data just got much easier. Your RAG application calls the MCP Server to run `list_segments` and converts the JSON output into searchable documents. Instead of guessing which group saw a specific feature announcement, you ask your agent. It searches the index and returns answers grounded in actual API data.

Track onboarding flows through the MCP Server

Documentation often drifts from what actually exists in production. By running `list_flows` and `get_flow`, your system builds a real-time knowledge base of every active tooltip and modal. When a product manager asks how a specific screen works, the agent pulls the exact configuration. The response relies on live data rather than outdated wiki pages.

Audit checklists and user progress

Support teams need context fast when tickets arrive. The application executes `get_user` to grab the profile and `list_checklists` to see where the customer got stuck. All that raw data gets embedded into your vector store. Your internal search tool now understands exactly what onboarding steps the user completed before complaining.

Setup guide

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

Set up your MCP connection using the BasicMCPClient with your Vinkius URL. Wrap it in McpToolSpec and pass those tools to your FunctionAgent for execution.

It certainly can. While RAG is the primary use case, the agent can execute unpublish_flow if it determines a specific guide is outdated based on your indexed documents.

It turns raw onboarding metrics into a conversational interface. Representatives can ask plain English questions about a user's progress and get immediate, accurate answers.

Not at all. The MCP standard handles the schema translation automatically. Your agent understands the tool signatures the moment it connects.

We run every request through an ephemeral zero-trust environment. Segment definitions, flow configurations, and user identifiers exist only during the transaction and leave no trace behind.

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