How to Use the GeTui / 个推 MCP in LlamaIndex

Q: How do I hook up GeTui / 个推 to LlamaIndex?

Install llama-index-tools-mcp via pip. Set up the MCP client pointing to the GeTui endpoint. Use McpToolSpec to convert the operations into tools your FunctionAgent can run.

Q: How do I limit which tools the agent can access?

Pass an allowedtools filter when configuring the MCP client. You might restrict an analytics agent to only run the reporting tools while blocking the push commands entirely.

Index GeTui push metrics into LlamaIndex with this MCP Server.

See Vinkius in Action

Works with every AI agent you already use

…and any MCP-compatible client

MCP Servers - Free for Subscribers

Connect GeTui / 个推 MCP to LlamaIndex

Create your Vinkius account to connect GeTui / 个推 to LlamaIndex and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

GDPR Free for Subscribers

Setup GeTui / 个推 with LlamaIndex

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Searchable campaign analytics

`get_daily_push_report` and `get_app_user_stats` pull raw performance data into your LlamaIndex knowledge base. Your application runs these tools, grabs the JSON metrics, and embeds them directly into your vector store. Now you can ask questions about past campaigns without writing SQL. You ask your agent why engagement dropped on Tuesday, and it queries the indexed GeTui reports to find the exact delivery failure rates.

RAG-driven audience building with this MCP Server

`get_user_tags` and `query_user_alias` feed device-level metadata into your semantic search pipeline. The agent pulls the tags for specific CIDs and stores them alongside your customer profiles. When you need to send a targeted alert, LlamaIndex retrieves the right aliases based on natural language queries. The function agent then executes `push_to_list` to hit that exact segment.

Real-time delivery verification

`get_push_status` and `get_cid_status` provide live state checks that your RAG application can reference. If a user complains about missing a notification, the agent queries their specific CID to see if they were online. Sending new messages works the same way. The agent calls `push_to_single` or `push_to_all` depending on the context it retrieved from the vector store. It grounds every push decision in actual historical data.

Setup guide

Set up GeTui / 个推 MCP in LlamaIndex

Prerequisites

Python 3.10+ installed
llama-index-tools-mcp package
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai. The MCP tools package provides BasicMCPClient and McpToolSpec.
2

Connect with BasicMCPClient
Point BasicMCPClient to your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports SSE and Streamable HTTP transports.
3

Convert to LlamaIndex tools
Call mcp_tool_spec.to_tool_list_async() to convert all GeTui / 个推 MCP tools into native FunctionTool objects that any LlamaIndex agent can use.
4

Run with any LLM
Create a FunctionAgent with the tools and your preferred LLM. Swap OpenAI for Anthropic, Gemini, or any LlamaIndex-supported provider.

agent.py

from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

# Connect to the MCP
mcp_client = BasicMCPClient(
    "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)
mcp_tool_spec = McpToolSpec(client=mcp_client)

# Convert MCP tools to LlamaIndex tools
tools = await mcp_tool_spec.to_tool_list_async()

# Create and run the agent
agent = FunctionAgent(
    tools=tools,
    llm=OpenAI(model="gpt-4o"),
    system_prompt="You have access to GeTui / 个推 tools.",
)
response = await agent.run("List recent GeTui / 个推 data")

Get your connection token →

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by GeTui / 个推. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

Why Choose Vinkius

Vinkius connects your tools to AI with real-time monitoring and automatic cost savings — all from one dashboard.

Connect GeTui / 个推 now

Real-time monitoring

Live

visibility into every interaction

Connect your favorite tools to your AI and see exactly what's happening — every request, every response, in real time.

Built-in savings

60%

lower AI costs

Vinkius compresses data between your apps and your AI automatically. Lower bills every month — no configuration required.

Single dashboard

One

place for every integration

Every tool your AI connects to, managed from a single screen. One account, complete control.

Common questions about GeTui / 个推 MCP in LlamaIndex

Install `llama-index-tools-mcp` via pip. Set up the MCP client pointing to the GeTui endpoint. Use `McpToolSpec` to convert the operations into tools your FunctionAgent can run.

It handles broadcasts perfectly. Your agent pulls the target audience from your documents and runs the push tool to execute the campaign.

Raw API data is hard to query. Embedding your daily push stats into a vector store lets you analyze campaign performance using plain English questions.

Pass an `allowed_tools` filter when configuring the MCP client. You might restrict an analytics agent to only run the reporting tools while blocking the push commands entirely.

The Vinkius zero-trust architecture isolates your environment completely. When the agent pulls user aliases or CIDs, that data flows through a secure, stateless tunnel. No logs remain on the host infrastructure after the query completes.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

OpenAI Agents SDK sdk-python

Google ADK sdk-python

Pydantic AI sdk-python

Vercel AI SDK sdk-typescript