ThinkingData / 数数科技 MCP Server for LangChain 8 tools — connect in under 2 minutes
LangChain is the leading Python framework for composable LLM applications. Connect ThinkingData / 数数科技 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({
"thinkingdata": {
"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 ThinkingData / 数数科技, 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 ThinkingData / 数数科技 MCP Server
Empower your AI agent to orchestrate your data analytics and player insights with ThinkingData (数数科技), the premier analytics platform for the global gaming industry. By connecting ThinkingData to your agent, you transform complex event querying, user profile auditing, and cohort management into a natural conversation. Your agent can instantly retrieve project metadata, list defined events and their schemas, execute complex behavioral queries, and even ingest custom events without you ever needing to navigate the comprehensive TA Dashboard. Whether you are conducting a player retention audit or coordinating a live-ops event refresh, your agent acts as a real-time data coordinator, providing accurate results from a single, authorized source.
LangChain's ecosystem of 500+ components combines seamlessly with ThinkingData / 数数科技 through native MCP adapters. Connect 8 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.
What you can do
- Project Orchestration — Retrieve project summaries, list defined events, and discover property schemas.
- Behavioral Auditing — Execute complex queries on event data to understand player behavior and conversion funnels.
- User Management — Query user profile data and update properties for specific distinct IDs.
- Data Ingestion — Track custom events and set user properties directly through the agent for rapid testing.
- Operational Insights — List defined user cohorts, saved reports, and monitor API connectivity status.
The ThinkingData / 数数科技 MCP Server exposes 8 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 ThinkingData / 数数科技 to LangChain via MCP
Follow these steps to integrate the ThinkingData / 数数科技 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 8 tools from ThinkingData / 数数科技 via MCP
Why Use LangChain with the ThinkingData / 数数科技 MCP Server
LangChain provides unique advantages when paired with ThinkingData / 数数科技 through the Model Context Protocol.
The largest ecosystem of integrations, chains, and agents — combine ThinkingData / 数数科技 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 ThinkingData / 数数科技 queries for multi-turn workflows
ThinkingData / 数数科技 + LangChain Use Cases
Practical scenarios where LangChain combined with the ThinkingData / 数数科技 MCP Server delivers measurable value.
RAG with live data: combine ThinkingData / 数数科技 tool results with vector store retrievals for answers grounded in both real-time and historical data
Autonomous research agents: LangChain agents query ThinkingData / 数数科技, synthesize findings, and generate comprehensive research reports
Multi-tool orchestration: chain ThinkingData / 数数科技 tools with web scrapers, databases, and calculators in a single agent run
Production monitoring: use LangSmith to trace every ThinkingData / 数数科技 tool call, measure latency, and optimize your agent's performance
ThinkingData / 数数科技 MCP Tools for LangChain (8)
These 8 tools become available when you connect ThinkingData / 数数科技 to LangChain via MCP:
get_event_properties
Get properties for event
get_project_summary
Get project overview
list_defined_events
List project events
list_project_cohorts
List user cohorts
query_events
Execute event query
query_users
Execute user query
set_user_properties
Update user profile
track_custom_event
Track a single event
Example Prompts for ThinkingData / 数数科技 in LangChain
Ready-to-use prompts you can give your LangChain agent to start working with ThinkingData / 数数科技 immediately.
"Show me a summary of our ThinkingData project configuration."
"List all defined events in ThinkingData."
"Check the profile for user 'USER_88210934'."
Troubleshooting ThinkingData / 数数科技 MCP Server with LangChain
Common issues when connecting ThinkingData / 数数科技 to LangChain through the Vinkius, and how to resolve them.
MultiServerMCPClient not found
pip install langchain-mcp-adaptersThinkingData / 数数科技 + LangChain FAQ
Common questions about integrating ThinkingData / 数数科技 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 ThinkingData / 数数科技 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 ThinkingData / 数数科技 to LangChain
Get your token, paste the configuration, and start using 8 tools in under 2 minutes. No API key management needed.