How to Use the FullStory MCP in LlamaIndex

Ground your LlamaIndex RAG applications in real user behavior by indexing FullStory session data for semantic search.

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Connect FullStory MCP to LlamaIndex

Create your Vinkius account to connect FullStory 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 FullStory with LlamaIndex

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Index session history in LlamaIndex

Convert your session recordings into a searchable knowledge base using `list_sessions`. You index the output so your agent finds answers grounded in actual user telemetry rather than guessing. This turns historical session data into a permanent index. Your agent queries this index to explain how users navigated your platform before a specific error occurred.

Retrieve behavioral profiles with LlamaIndex

Call `get_user` to pull raw interaction data into your vector store. You gain a semantic understanding of how specific users behave across devices. LlamaIndex then links these profiles to your existing documentation. When a user reports a bug, your agent searches both your help articles and these behavioral profiles to find the root cause.

Search segments using LlamaIndex

Use `list_segments` to fetch your saved audience filters and add them to your index. The model understands which user groups are active based on the data retrieved from the tool. It then uses this information to filter its search results when you ask about platform performance. You get answers that are specific to the segments you care about.

Setup guide

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

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Common questions about FullStory MCP in LlamaIndex

The MCP tools return structured JSON that LlamaIndex parses into nodes. You then add these nodes to your vector store for semantic retrieval.

Absolutely. Once the session metadata is in your index, you ask natural language questions about past user behavior and the agent pulls the relevant recordings.

The data resides in your vector store as text representations. You control the scope of what gets indexed by filtering the tool output before it hits your database.

You trigger periodic syncs using the MCP tools. The agent fetches the latest sessions and updates the index to reflect recent platform activity.

FullStory data is stored according to your privacy rules. You use `delete_user` to remove specific records, and the agent's index should be refreshed to prune that data.

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