How to Use the Audienceful MCP in LlamaIndex

Q: How do I add Audienceful tools to a LlamaIndex application?

Install llama-index-tools-mcp via pip. Initialize a BasicMCPClient with your Vinkius URL, convert it using McpToolSpec, and pass the async list to your FunctionAgent.

Q: Can LlamaIndex search through Audienceful email send reports?

Yes, by calling listsendreports and embedding the output. Your RAG setup can then answer questions about open rates and click performance based on actual historical data.

Q: How does LlamaIndex update Audienceful subscriber profiles?

The agent retrieves the current state using getperson, determines what needs changing based on your query, and executes updateperson with the new values.

Q: Can I restrict which Audienceful tools my LlamaIndex agent uses?

Use the allowedtools filter during setup. You might permit listpeople for indexing while blocking deleteperson to prevent accidental data loss.

Index Audienceful subscriber profiles and campaign reports into LlamaIndex to build queryable marketing RAG applications.

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Create your Vinkius account to connect Audienceful 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 Audienceful with LlamaIndex

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Indexing Audienceful MCP Server Data

The Audienceful MCP Server feeds live marketing data straight into your vector stores. Your LlamaIndex application calls `list_people` and `list_send_reports`, converting the raw JSON responses into searchable documents. Querying past campaigns no longer relies on stale exports. When a user asks about recent newsletter performance, the engine retrieves grounded answers directly from the indexed API data instead of hallucinating metrics.

Grounded Subscriber Profile Search

Fetching specific user histories requires precision, which `get_person` delivers to your RAG pipeline. The agent pulls a UID, extracts the profile details, and embeds the context alongside your internal CRM notes. Marketers can ask natural language questions about custom attributes because `list_custom_fields` maps the available data structure. The engine knows exactly what fields exist before attempting to filter or summarize audience segments.

Actionable Marketing Knowledge Bases

Combining read and write operations turns static indexes into active agents. After analyzing engagement trends, your FunctionAgent executes `update_person` to tag high-value subscribers or runs `trigger_automation` to start a re-engagement loop. Passing `include_resources=True` ensures the LLM has full context before modifying any records. Creating new segments via `create_custom_field` happens only after the system verifies the field does not already exist in the knowledge base.

Setup guide

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

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Common questions about Audienceful MCP in LlamaIndex

Install `llama-index-tools-mcp` via pip. Initialize a `BasicMCPClient` with your Vinkius URL, convert it using `McpToolSpec`, and pass the async list to your `FunctionAgent`.

Yes, by calling `list_send_reports` and embedding the output. Your RAG setup can then answer questions about open rates and click performance based on actual historical data.

The agent retrieves the current state using `get_person`, determines what needs changing based on your query, and executes `update_person` with the new values.

Use the `allowed_tools` filter during setup. You might permit `list_people` for indexing while blocking `delete_person` to prevent accidental data loss.

The connection uses a single endpoint token with auth managed entirely outside your code. Subscriber UIDs, email addresses, and campaign metrics travel through an isolated, stateless sandbox straight to your local vector store.

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