How to Use the AdButler MCP in LlamaIndex

Q: Can I index AdButler performance metrics into a vector database using LlamaIndex?

Yes. You can call getperformancereport to retrieve active metrics and load the resulting JSON directly into your document store for vector indexing.

Q: How does LlamaIndex use the AdButler creative library?

The agent uses listcreatives to fetch your asset metadata. It then indexes these details, allowing you to run semantic search queries to find specific banners or video placements.

Index live AdButler ad data into LlamaIndex vector stores using this MCP Server for semantic search and grounded RAG queries.

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Connect AdButler MCP to LlamaIndex

Create your Vinkius account to connect AdButler 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 AdButler with LlamaIndex

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LlamaIndex RAG for AdButler inventory

`list_creatives` pulls your entire library of banners and video assets directly into your index. Your agent searches this library semantically, matching specific creative assets to current campaign briefs without manual tagging. By feeding this data into your vector store, you query your creative assets using plain English. The agent finds the exact files you need based on past performance or asset descriptions.

Grounded performance analysis

`get_performance_report` feeds raw impression and click metrics directly into your query engine. This keeps your agent's answers grounded in actual numbers, preventing hallucinated performance stats. Instead of guessing how a campaign is doing, the agent builds a temporary index of the report. You get answers backed by hard CTR data from your active zones.

Publisher and zone indexing via MCP Server

`list_publishers` and `list_zones` let you index your entire network structure. The agent maps publishers to their respective active zones, creating a searchable directory of your ad placements. When you need to find placements for a new campaign, the agent queries this index to find publishers with matching zones. This eliminates manual spreadsheet lookups.

Setup guide

Set up AdButler 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 AdButler 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 AdButler tools.",
)
response = await agent.run("List recent AdButler 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 AdButler. 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 AdButler MCP in LlamaIndex

Install the MCP tool package using pip. Initialize BasicMCPClient with the Vinkius URL, wrap it in McpToolSpec, and call to_tool_list_async to get your tools.

Yes. You can call get_performance_report to retrieve active metrics and load the resulting JSON directly into your document store for vector indexing.

The agent uses list_creatives to fetch your asset metadata. It then indexes these details, allowing you to run semantic search queries to find specific banners or video placements.

Yes, the client setup supports an allowed tools filter. You can restrict your agent to specific operations if needed.

Vinkius executes the code in ephemeral, zero-trust sandboxes. Your publisher IDs and zone lists are fetched on demand and never cached or exposed to external parties.

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