Umeng / 友盟+ 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 Umeng / 友盟+ 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 Umeng / 友盟+. "
"You have 10 tools available."
),
)
response = await agent.run(
"What tools are available in Umeng / 友盟+?"
)
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 Umeng / 友盟+ MCP Server
Empower your AI agent to orchestrate your mobile growth and engagement with Umeng+ (友盟+), the premier mobile infrastructure provider in China. By connecting Umeng to your agent, you transform complex push notification campaigns and deep analytical auditing into a natural conversation. Your agent can instantly send targeted push messages, retrieve real-time delivery status, monitor user retention trends, and even provide high-level application performance summaries without you ever needing to navigate the comprehensive Umeng portal. Whether you are conducting a growth audit or coordinating a cross-functional marketing blast, your agent acts as a real-time mobile operations assistant, keeping your data accurate and your users engaged.
LlamaIndex agents combine Umeng / 友盟+ tool responses with indexed documents for comprehensive, grounded answers. Connect 10 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
- Push Orchestration — Send template-based or custom push notifications and retrieve real-time delivery and click status.
- User Growth Auditing — Retrieve detailed metrics for active users, new registrations, and retention across any time period.
- Behavioral Analysis — Browse custom event data and session duration statistics to identify user engagement patterns.
- Task Management — List recent push tasks and cancel pending operations directly through the agent interface.
- Performance Insights — Access high-level application summaries to monitor the health and growth of your mobile ecosystem.
The Umeng / 友盟+ 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 Umeng / 友盟+ to LlamaIndex via MCP
Follow these steps to integrate the Umeng / 友盟+ 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 Umeng / 友盟+
Why Use LlamaIndex with the Umeng / 友盟+ MCP Server
LlamaIndex provides unique advantages when paired with Umeng / 友盟+ through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Umeng / 友盟+ tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Umeng / 友盟+ tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Umeng / 友盟+, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Umeng / 友盟+ tools were called, what data was returned, and how it influenced the final answer
Umeng / 友盟+ + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Umeng / 友盟+ MCP Server delivers measurable value.
Hybrid search: combine Umeng / 友盟+ real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Umeng / 友盟+ 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 Umeng / 友盟+ for fresh data
Analytical workflows: chain Umeng / 友盟+ queries with LlamaIndex's data connectors to build multi-source analytical reports
Umeng / 友盟+ MCP Tools for LlamaIndex (10)
These 10 tools become available when you connect Umeng / 友盟+ to LlamaIndex via MCP:
cancel_push
Cancel pending push task
get_active_users
Get active user count
get_app_summary
Get app analytics summary
get_duration_stats
Get session duration stats
get_event_data
Get custom event data
get_new_users
Get new user registrations
get_push_status
Check push task status
get_retention
Get user retention stats
list_push_tasks
List recent push tasks
send_push
Send push notification
Example Prompts for Umeng / 友盟+ in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Umeng / 友盟+ immediately.
"Send a broadcast push saying 'Flash Sale starts now!' to all users."
"Show me the active user metrics for today."
"What is the retention rate for users who joined last Monday?"
Troubleshooting Umeng / 友盟+ MCP Server with LlamaIndex
Common issues when connecting Umeng / 友盟+ to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpUmeng / 友盟+ + LlamaIndex FAQ
Common questions about integrating Umeng / 友盟+ 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 Umeng / 友盟+ 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 Umeng / 友盟+ to LlamaIndex
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.