How to Use the MoEngage MCP in LlamaIndex

Q: Does the MoEngage MCP Server allow LlamaIndex to update customer profiles?

Yes, upsertcustomer is available for your agent to modify records. It’s part of the tool list that LlamaIndex loads into the agent's function set.

Q: How do I filter which MoEngage tools LlamaIndex can access?

Use the allowedtools parameter when initializing your tool spec. This restricts the agent to only the functions you want it to use.

Turn MoEngage API data into searchable context for your LlamaIndex knowledge base.

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Connect MoEngage MCP to LlamaIndex

Create your Vinkius account to connect MoEngage 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 MoEngage with LlamaIndex

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Index MoEngage campaign metrics with LlamaIndex

Use `get_campaign_stats` to pull performance numbers and feed them directly into your vector store. This grounds your agent's analysis in actual historical data rather than estimates. Your RAG application indexes these results to answer questions about past performance. It creates a searchable record of every campaign you run.

Search customer profiles via LlamaIndex

Your agent uses `search_customers` to find specific users and then indexes their profile attributes into your knowledge base. This allows the AI to recall specific user attributes during a query. This turns your customer data into a queryable asset. You can ask questions about your user base and get answers based on the latest API state.

Query segment definitions with LlamaIndex

Call `get_segment` to retrieve criteria and store them as searchable context. Your agent uses this to understand exactly who falls into which group for your RAG-based analysis. This maps your audience logic into your index. You get a clear view of your segments without needing to navigate the dashboard manually.

Setup guide

Set up MoEngage 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 MoEngage 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 MoEngage tools.",
)
response = await agent.run("List recent MoEngage data")

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Common questions about MoEngage MCP in LlamaIndex

The MCP client retrieves the raw data, which LlamaIndex then processes into nodes. You can then add these nodes to your vector index for semantic search.

Absolutely, the tools provide live API data that LlamaIndex treats as a source of truth. Your agent uses this data to answer queries about campaigns or customers.

Yes, `upsert_customer` is available for your agent to modify records. It’s part of the tool list that LlamaIndex loads into the agent's function set.

Use the allowed_tools parameter when initializing your tool spec. This restricts the agent to only the functions you want it to use.

We handle only the transient data transmitted during your tool calls. Your vector index remains local to your environment, keeping your customer identification data private.

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