ThinkingData / 数数科技 MCP Server for LlamaIndex 8 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add ThinkingData / 数数科技 as an MCP tool provider through the Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
ASK AI ABOUT THIS MCP SERVER
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 ThinkingData / 数数科技. "
"You have 8 tools available."
),
)
response = await agent.run(
"What tools are available in ThinkingData / 数数科技?"
)
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 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.
LlamaIndex agents combine ThinkingData / 数数科技 tool responses with indexed documents for comprehensive, grounded answers. Connect 8 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.
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 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 ThinkingData / 数数科技 to LlamaIndex via MCP
Follow these steps to integrate the ThinkingData / 数数科技 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 8 tools from ThinkingData / 数数科技
Why Use LlamaIndex with the ThinkingData / 数数科技 MCP Server
LlamaIndex provides unique advantages when paired with ThinkingData / 数数科技 through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine ThinkingData / 数数科技 tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain ThinkingData / 数数科技 tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query ThinkingData / 数数科技, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what ThinkingData / 数数科技 tools were called, what data was returned, and how it influenced the final answer
ThinkingData / 数数科技 + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the ThinkingData / 数数科技 MCP Server delivers measurable value.
Hybrid search: combine ThinkingData / 数数科技 real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query ThinkingData / 数数科技 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 ThinkingData / 数数科技 for fresh data
Analytical workflows: chain ThinkingData / 数数科技 queries with LlamaIndex's data connectors to build multi-source analytical reports
ThinkingData / 数数科技 MCP Tools for LlamaIndex (8)
These 8 tools become available when you connect ThinkingData / 数数科技 to LlamaIndex 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 LlamaIndex
Ready-to-use prompts you can give your LlamaIndex 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 LlamaIndex
Common issues when connecting ThinkingData / 数数科技 to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpThinkingData / 数数科技 + LlamaIndex FAQ
Common questions about integrating ThinkingData / 数数科技 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 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 LlamaIndex
Get your token, paste the configuration, and start using 8 tools in under 2 minutes. No API key management needed.