FullStory MCP Server for LangChain 11 tools — connect in under 2 minutes
LangChain is the leading Python framework for composable LLM applications. Connect FullStory through the Vinkius and LangChain agents can call every tool natively — combine them with retrievers, memory, and output parsers for sophisticated AI pipelines.
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Vinkius supports streamable HTTP and SSE.
import asyncio
from langchain_mcp_adapters.client import MultiServerMCPClient
from langchain_openai import ChatOpenAI
from langgraph.prebuilt import create_react_agent
async def main():
# Your Vinkius token — get it at cloud.vinkius.com
async with MultiServerMCPClient({
"fullstory": {
"transport": "streamable_http",
"url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
}
}) as client:
tools = client.get_tools()
agent = create_react_agent(
ChatOpenAI(model="gpt-4o"),
tools,
)
response = await agent.ainvoke({
"messages": [{
"role": "user",
"content": "Using FullStory, show me what tools are available.",
}]
})
print(response["messages"][-1].content)
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.
LangChain's ecosystem of 500+ components combines seamlessly with FullStory through native MCP adapters. Connect 11 tools via the Vinkius and use ReAct agents, Plan-and-Execute strategies, or custom agent architectures — with LangSmith tracing giving full visibility into every tool call, latency, and token cost.
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 LangChain 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 LangChain via MCP
Follow these steps to integrate the FullStory MCP Server with LangChain.
Install dependencies
Run pip install langchain langchain-mcp-adapters langgraph langchain-openai
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token
Run the agent
Save the code and run python agent.py
Explore tools
The agent discovers 11 tools from FullStory via MCP
Why Use LangChain with the FullStory MCP Server
LangChain provides unique advantages when paired with FullStory through the Model Context Protocol.
The largest ecosystem of integrations, chains, and agents — combine FullStory MCP tools with 500+ LangChain components
Agent architecture supports ReAct, Plan-and-Execute, and custom strategies with full MCP tool access at every step
LangSmith tracing gives you complete visibility into tool calls, latencies, and token usage for production debugging
Memory and conversation persistence let agents maintain context across FullStory queries for multi-turn workflows
FullStory + LangChain Use Cases
Practical scenarios where LangChain combined with the FullStory MCP Server delivers measurable value.
RAG with live data: combine FullStory tool results with vector store retrievals for answers grounded in both real-time and historical data
Autonomous research agents: LangChain agents query FullStory, synthesize findings, and generate comprehensive research reports
Multi-tool orchestration: chain FullStory tools with web scrapers, databases, and calculators in a single agent run
Production monitoring: use LangSmith to trace every FullStory tool call, measure latency, and optimize your agent's performance
FullStory MCP Tools for LangChain (11)
These 11 tools become available when you connect FullStory to LangChain 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 LangChain
Ready-to-use prompts you can give your LangChain 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 LangChain
Common issues when connecting FullStory to LangChain through the Vinkius, and how to resolve them.
MultiServerMCPClient not found
pip install langchain-mcp-adaptersFullStory + LangChain FAQ
Common questions about integrating FullStory MCP Server with LangChain.
How does LangChain connect to MCP servers?
langchain-mcp-adapters to create an MCP client. LangChain discovers all tools and wraps them as native LangChain tools compatible with any agent type.Which LangChain agent types work with MCP?
Can I trace MCP tool calls in LangSmith?
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 LangChain
Get your token, paste the configuration, and start using 11 tools in under 2 minutes. No API key management needed.