TalkingData MCP Server for LlamaIndex 10 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add TalkingData as an MCP tool provider through 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 TalkingData. "
"You have 10 tools available."
),
)
response = await agent.run(
"What tools are available in TalkingData?"
)
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 TalkingData MCP Server
Empower your AI agent to orchestrate your product intelligence and user behavioral data with TalkingData, the premier big data platform in China. By connecting TalkingData to your agent, you transform complex event tracking auditing, user growth analysis, and multi-channel attribution into a natural conversation. Your agent can instantly retrieve high-level application summaries, monitor active user trends, audit custom event data, and even provide detailed hardware device statistics without you ever needing to navigate the comprehensive TalkingData portal. Whether you are conducting a product health audit or monitoring real-time campaign performance across different channels, your agent acts as a real-time data analyst assistant, keeping your metrics accurate and your growth moving.
LlamaIndex agents combine TalkingData tool responses with indexed documents for comprehensive, grounded answers. Connect 10 tools through 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
- Metrics Orchestration — Retrieve real-time active user, new registration, and retention statistics for your application.
- Event Auditing — Browse tracked custom events and retrieve detailed analytical data for specific behavioral triggers.
- Channel Monitoring — Analyze user acquisition and performance across different marketing and distribution channels.
- Hardware Insights — Access detailed breakdowns of user devices, including model, OS, and hardware specifications.
- Session Analysis — Retrieve average session duration and usage frequency to monitor product engagement levels.
The TalkingData MCP Server exposes 10 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 TalkingData to LlamaIndex via MCP
Follow these steps to integrate the TalkingData 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 10 tools from TalkingData
Why Use LlamaIndex with the TalkingData MCP Server
LlamaIndex provides unique advantages when paired with TalkingData through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine TalkingData tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain TalkingData tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query TalkingData, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what TalkingData tools were called, what data was returned, and how it influenced the final answer
TalkingData + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the TalkingData MCP Server delivers measurable value.
Hybrid search: combine TalkingData real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query TalkingData 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 TalkingData for fresh data
Analytical workflows: chain TalkingData queries with LlamaIndex's data connectors to build multi-source analytical reports
TalkingData MCP Tools for LlamaIndex (10)
These 10 tools become available when you connect TalkingData to LlamaIndex via MCP:
get_active_users
Get active user statistics
get_app_info
Get application configuration info
get_app_summary
Get application performance summary
get_channel_data
Get acquisition channel data
get_device_stats
Get device hardware statistics
get_event_data
Get custom event analytics
get_new_users
Get new user registrations
get_session_stats
Get session usage statistics
get_user_retention
Get user retention data
list_events
List tracked events
Example Prompts for TalkingData in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with TalkingData immediately.
"Show me the application performance summary."
"What is the active user count for today?"
"List all tracked events in the app."
Troubleshooting TalkingData MCP Server with LlamaIndex
Common issues when connecting TalkingData to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpTalkingData + LlamaIndex FAQ
Common questions about integrating TalkingData 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 TalkingData 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 TalkingData to LlamaIndex
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.