AdRoll MCP Server for LlamaIndex 5 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add AdRoll as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
ASK AI ABOUT THIS MCP SERVER
Vinkius supports streamable HTTP and SSE.
import asyncio
from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI
async def main():
# Your Vinkius token. get it at cloud.vinkius.com
mcp_client = BasicMCPClient("https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
mcp_tool_spec = McpToolSpec(client=mcp_client)
tools = await mcp_tool_spec.to_tool_list_async()
agent = FunctionAgent(
tools=tools,
llm=OpenAI(model="gpt-4o"),
system_prompt=(
"You are an assistant with access to AdRoll. "
"You have 5 tools available."
),
)
response = await agent.run(
"What tools are available in AdRoll?"
)
print(response)
asyncio.run(main())
* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure
About AdRoll MCP Server
Connect your AdRoll (NextRoll) account to your AI agent to unlock professional e-commerce marketing and retargeting orchestration. From auditing your advertisable accounts to monitoring real-time campaign performance and managing creative ad assets, your agent handles your advertising ecosystem through natural conversation.
LlamaIndex agents combine AdRoll tool responses with indexed documents for comprehensive, grounded answers. Connect 5 tools through Vinkius and query live data alongside vector stores and SQL databases in a single turn. ideal for hybrid search, data enrichment, and analytical workflows.
What you can do
- Campaign Management — List and retrieve details for active campaigns across multiple channels (Web, Facebook, etc.)
- Account Auditing — List your 'Advertisables' (advertiser accounts) and retrieve technical metadata for each
- Performance Reporting — Retrieve granular statistics on clicks, spend, and conversions to monitor your marketing ROI
- Creative Asset Oversight — List and audit your ad creatives (banners, videos) to ensure your visual content is optimized
- Strategy Insights — Quickly identify high-performing strategies and identify areas for budget optimization directly from chat
The AdRoll MCP Server exposes 5 tools through the Vinkius. Connect it to LlamaIndex in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.
How to Connect AdRoll to LlamaIndex via MCP
Follow these steps to integrate the AdRoll MCP Server with LlamaIndex.
Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token
Run the agent
Save to agent.py and run: python agent.py
Explore tools
The agent discovers 5 tools from AdRoll
Why Use LlamaIndex with the AdRoll MCP Server
LlamaIndex provides unique advantages when paired with AdRoll through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine AdRoll tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain AdRoll tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query AdRoll, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what AdRoll tools were called, what data was returned, and how it influenced the final answer
AdRoll + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the AdRoll MCP Server delivers measurable value.
Hybrid search: combine AdRoll real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query AdRoll to augment indexed data with live information before generating user-facing responses
Knowledge base agents: build agents that maintain and update knowledge bases by periodically querying AdRoll for fresh data
Analytical workflows: chain AdRoll queries with LlamaIndex's data connectors to build multi-source analytical reports
AdRoll MCP Tools for LlamaIndex (5)
These 5 tools become available when you connect AdRoll to LlamaIndex via MCP:
get_campaign_details
Get campaign metadata
get_performance_report
Filterable by EID and date. Get performance statistics
list_ads
List ad creatives
list_advertisables
List advertiser accounts
list_campaigns
List active campaigns
Example Prompts for AdRoll in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with AdRoll immediately.
"List all active advertisable accounts in my AdRoll organization."
"Show me the performance of my Web Retargeting campaign for the last 7 days."
"List all ad creatives for advertisable EID 'AD1234567890'."
Troubleshooting AdRoll MCP Server with LlamaIndex
Common issues when connecting AdRoll to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpAdRoll + LlamaIndex FAQ
Common questions about integrating AdRoll MCP Server with LlamaIndex.
How does LlamaIndex connect to MCP servers?
Can I combine MCP tools with vector stores?
Does LlamaIndex support async MCP calls?
Connect AdRoll with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
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GitHub Copilot in VS Code with Agent mode and MCP support.
Purpose-built IDE for agentic AI coding workflows.
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Anthropic's agentic CLI for terminal-first development.
Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
Type-safe agent development for Python with first-class MCP support.
TypeScript toolkit for building AI-powered web applications.
TypeScript-native agent framework for modern web stacks.
Python framework for orchestrating collaborative AI agent crews.
Leading Python framework for composable LLM applications.
Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
Connect AdRoll to LlamaIndex
Get your token, paste the configuration, and start using 5 tools in under 2 minutes. No API key management needed.