FullStory MCP Server for LlamaIndex 11 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add FullStory as an MCP tool provider through the Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
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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 FullStory. "
"You have 11 tools available."
),
)
response = await agent.run(
"What tools are available in FullStory?"
)
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 FullStory MCP Server
Connect your FullStory account to any AI agent to automate your digital experience intelligence (DXI) and product analytics workflows through the Model Context Protocol (MCP). FullStory provides a comprehensive view of how users interact with your site or app. This MCP server enables you to manage user profiles, track server-side events, and retrieve session metadata and playback links directly through natural conversation.
LlamaIndex agents combine FullStory tool responses with indexed documents for comprehensive, grounded answers. Connect 11 tools through the 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
- User Lifecycle Management — List all captured users, fetch detailed profile metadata, and upsert records to maintain accurate identity mapping.
- Session Oversight — Retrieve a list of recording sessions for specific users and access playback URLs to visualize the customer journey.
- Interaction Events — Fetch the complete set of captured events (clicks, navigations, custom events) for any specific session ID.
- Server-Side Tracking — Log custom interaction events programmatically from your backend to enrich your FullStory datasets.
- Segment Discovery — Access and list configured user segments to understand high-level audience behavior.
- Data Exports — Monitor and list generated raw data bundles for deeper analytical processing.
- Privacy Compliance — Programmatically delete user data to support GDPR and CCPA requests via simple AI commands.
The FullStory MCP Server exposes 11 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 FullStory to LlamaIndex via MCP
Follow these steps to integrate the FullStory 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 11 tools from FullStory
Why Use LlamaIndex with the FullStory MCP Server
LlamaIndex provides unique advantages when paired with FullStory through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine FullStory tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain FullStory tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query FullStory, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what FullStory tools were called, what data was returned, and how it influenced the final answer
FullStory + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the FullStory MCP Server delivers measurable value.
Hybrid search: combine FullStory real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query FullStory 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 FullStory for fresh data
Analytical workflows: chain FullStory queries with LlamaIndex's data connectors to build multi-source analytical reports
FullStory MCP Tools for LlamaIndex (11)
These 11 tools become available when you connect FullStory to LlamaIndex via MCP:
create_event
Automatically mapped for server-side metrics injection that bypassed the frontal JavaScript client. Dispatch a custom backend event into FullStory
create_update_user
Used to synchronize custom tenant data into the FullStory analytics boundary for enhanced session context. Upsert a user identity into FullStory tracking
delete_user
Permanently removes privacy-sensitive telemetry, custom property links, and session aggregations inside FullStory. Erase a user profile and their associated session history
get_account_info
Validates live integration capability and fetches workspace scope definitions. Verify authentication and retrieve basic platform stats
get_session
Resolves page transitions, total active time, event markers, and active direct access tracking links. Retrieve deep metadata for a specific session recording
get_session_events
Exposes click trails, navigation mutations, error logs, and precise structural DOM changes. Fetch the chronological telemetry events for a session
get_user
Retrieves raw session metadata, total session durations, custom variables, and cross-device interaction boundaries. Get full tracking profile and behavior history for a specific user
list_exports
Resolves the status of bulk data offloads, processing states, and download URIs for warehouse ingestion. List raw data export jobs available for download
list_segments
Resolves saved audience definitions, population counts across the last 30 days, and logical filter structures. List dynamic audience segments built in FullStory
list_sessions
Resolves a subset of sessions matching provided JSON query criteria, exposing playback links, browser signatures, and metric overlays. List or search session recordings based on telemetry
list_users
Resolves user identities, custom parameters, session counts, and aggregate behavioral profiles across the analytics boundary. Query the FullStory subscriber and visitor directory for analytics
Example Prompts for FullStory in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with FullStory immediately.
"List the last 5 sessions for user UID 'user_12345'."
"Show me the full profile details for FullStory ID '123:456'."
"Track a server event 'Subscription Renewed' for user 'johndoe@email.com'."
Troubleshooting FullStory MCP Server with LlamaIndex
Common issues when connecting FullStory to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpFullStory + LlamaIndex FAQ
Common questions about integrating FullStory 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 FullStory 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 FullStory to LlamaIndex
Get your token, paste the configuration, and start using 11 tools in under 2 minutes. No API key management needed.