How to Use the Jiguang Aurora / 极光 MCP in LlamaIndex

Q: Can LlamaIndex query historical push reports from Jiguang Aurora / 极光?

Yes. Your agent pulls reports using getpushreport and indexes the results. You can then run semantic queries over your delivery history.

Q: How does LlamaIndex prevent sending duplicate notifications via Jiguang Aurora / 极光?

The agent checks your vector store for recent sendpush logs. If a similar message was sent recently, it skips the execution to protect the user experience.

Index Jiguang Aurora / 极光 notification logs directly into your LlamaIndex vector store for data-grounded push strategies.

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Connect Jiguang Aurora / 极光 MCP to LlamaIndex

Create your Vinkius account to connect Jiguang Aurora / 极光 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.

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Setup Jiguang Aurora / 极光 with LlamaIndex

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Build a searchable knowledge base with this MCP Server

Stop guessing why your campaigns fail. Your agent calls `get_push_report` and `get_user_report`, then indexes the raw text directly into your vector store. You query your history using natural language to find out which messages performed best. This turns ephemeral API outputs into permanent, searchable knowledge. Your agent reads actual historical performance data instead of hallucinating what worked in the past.

Ground your messaging decisions in live device data

Before sending a message, your agent queries the vector store to check past device interactions. It pulls fresh data using `get_device_info` and updates the index with the latest tags. This keeps your context window packed with real, verified user states. You avoid sending duplicate messages. The agent checks the index first, sees what was sent, and uses `send_push` only when the user is actually due for an update.

Run automated audit loops with this MCP Server

Audit your active schedules using semantic search. The agent lists your active queues with `list_schedules` and matches them against your marketing calendar indexed in LlamaIndex. If it finds a mismatch, it calls `delete_schedule` to kill the rogue task. This prevents embarrassing duplicate notifications. You get a self-correcting system that verifies scheduled tasks against your actual business documentation.

Setup guide

Set up Jiguang Aurora / 极光 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 Jiguang Aurora / 极光 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 Jiguang Aurora / 极光 tools.",
)
response = await agent.run("List recent Jiguang Aurora / 极光 data")

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Jiguang Aurora / 极光. 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.

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Common questions about Jiguang Aurora / 极光 MCP in LlamaIndex

Run pip install llama-index-tools-mcp and instantiate the client. Use the tool spec to convert the server endpoints into tools your function agent can use.

Yes. Your agent pulls reports using get_push_report and indexes the results. You can then run semantic queries over your delivery history.

The agent checks your vector store for recent send_push logs. If a similar message was sent recently, it skips the execution to protect the user experience.

Yes, you can use the allowed tools filter in the tool spec. This MCP Server setup lets you restrict your agent to read-only tools if you want to prevent accidental push sends.

It processes device registration IDs and user tags inside an ephemeral sandbox. No data is stored on external servers, keeping your Chinese user database compliant with local privacy laws.

Use it with your favorite AI tools

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