How to Use the Measured MCP in LlamaIndex

Index your Measured marketing analytics into LlamaIndex to build agents that recall past performance and gain contextual insights.

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Connect Measured MCP to LlamaIndex

Create your Vinkius account to connect Measured 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 Measured with LlamaIndex

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Index performance data for LlamaIndex

Transform raw marketing reports into a searchable knowledge base. By running `list_reports` and `get_performance_summary`, you build a record that LlamaIndex can query later. This creates a system where the AI remembers how specific campaigns performed last quarter. It grounds current strategy in actual historical results rather than hallucinated projections.

Query live channel metrics with LlamaIndex

Use the MCP tools to fetch real-time data from `get_performance_by_channel`. LlamaIndex indexes these results so your agent can compare current performance against stored knowledge. You get answers that reflect the latest marketing reality. The agent combines live API data with your indexed documents to form a complete picture of your brand's reach.

Retrieve deeper insights automatically

Trigger `get_insights` to let the agent pull specific trends for your review. LlamaIndex stores these insights as vectors, making it simple to find patterns across multiple marketing channels. You can ask the agent about specific performance shifts over time. It retrieves the relevant data points from the index to support its analysis.

Setup guide

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

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Common questions about Measured MCP in LlamaIndex

Yes. The MCP tools provide the data which LlamaIndex then converts into vectors. This allows your agent to perform semantic searches across your marketing reports.

It is straightforward. You use the MCP client to pull the tools, which then become part of your agent's toolkit. LlamaIndex handles the indexing of the resulting data.

It gives you a searchable history of your marketing performance. You no longer have to dig through old spreadsheets because the agent has the data indexed and ready.

It handles performance metrics, campaign metadata, and incrementality scores. These are all processed as structured JSON before being indexed by your agent.

Your marketing data stays within the ephemeral sandbox of the server. Only the agent authorized with your specific token can fetch the information for indexing.

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