How to Use the Amazon DSP MCP in LlamaIndex

Index live Amazon DSP ad configurations into searchable LlamaIndex vector stores for grounded campaign analysis.

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Connect Amazon DSP MCP to LlamaIndex

Create your Vinkius account to connect Amazon DSP 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 Amazon DSP with LlamaIndex

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Turn campaigns into queryable nodes

Stop guessing how your programmatic buys are structured. Your LlamaIndex application fetches live data using `list_campaigns` and `list_ad_groups`, then embeds those structures directly into your vector store. Now you can ask your agent plain-text questions about your setup. Because the answers are grounded in actual API responses from the MCP integration, you get factual readouts of your ad groups instead of hallucinated garbage.

Ground RAG apps with this MCP Server

High-level summaries rarely satisfy media buyers. By exposing `get_campaign_details` to your function agent, LlamaIndex drills down into specific flight dates, budgets, and targeting parameters on demand. The system combines these deep details with your internal strategy documents. You get a unified index where your historical media plans sit right next to real-time execution data.

Synthesize performance and audience data

Media reporting usually requires exporting CSVs and building pivot tables. LlamaIndex lets you trigger `request_report` and pull the finished data via `get_report_status` straight into a document index. The agent correlates those performance metrics with active segments from `list_audiences`. Ask which targeting groups drove the highest yield, and the engine synthesizes the answer from the indexed tool outputs.

Setup guide

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

Install the tools package. Set up a basic client with the Vinkius URL, wrap it in a tool spec, and pass the async tool list to your function agent.

It handles the entire extraction process. The agent requests the report, waits for it to finish, and indexes the resulting metrics for natural language querying.

Data loaders are static. The MCP protocol gives your agent interactive tools to fetch fresh campaign details or check report statuses exactly when the user asks a question.

You restrict the agent's capabilities using the allowed tools parameter. If you only want it reading audiences, just block the reporting and campaign list functions.

Vinkius operates on a zero-trust architecture. Your spend data and creative assets route through an isolated execution context that self-destructs after the tool call completes. Nothing is ever stored on our infrastructure.

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