How to Use the Chewy Ads MCP in LlamaIndex

Q: Can I use the Chewy Ads MCP Server with LlamaIndex sub-question query engines?

Yes, you can register the tools using McpToolSpec and pass them to a FunctionAgent. The engine will break down complex queries and call listchewycampaigns to answer specific sub-questions.

Q: Does the Chewy Ads server support LlamaIndex asynchronous tool calls?

Yes, you can use totoollistasync() to load the tools into your LlamaIndex environment. This keeps your data retrieval loops fast when fetching heavy performance reports.

Index live Chewy Ads campaign data via MCP into your LlamaIndex vector store for grounded, context-aware retail media decisions.

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Connect Chewy Ads MCP to LlamaIndex

Create your Vinkius account to connect Chewy Ads 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 Chewy Ads with LlamaIndex

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Index Chewy Ads catalogs for LlamaIndex RAG pipelines

`list_chewy_catalogs` pulls your live product listings into your LlamaIndex pipeline so your agent can index them for semantic search. This allows your LlamaIndex query engine to retrieve actual Chewy catalog data instead of relying on outdated offline documents. Combining this tool with `list_campaign_adgroups` lets your LlamaIndex RAG system map your physical Chewy catalog items to active ad groups. Your LlamaIndex agent can then answer complex questions about which products are actively advertised on Chewy.

Build historical performance indexes with LlamaIndex

`get_performance_report` extracts raw metrics for specific periods, which LlamaIndex can index directly into a vector store. This turns flat Chewy performance numbers into a searchable LlamaIndex knowledge base of your ad history. When you query your LlamaIndex agent about past Chewy campaign trends, it searches these indexed reports to give you grounded answers. You get precise Chewy ad historical comparisons via this MCP Server in your LlamaIndex environment without manually parsing CSV exports.

Query Chewy Ads keyword performance semantically

`list_adgroup_keywords` fetches active search terms and their metrics so your LlamaIndex agent can index them for search analysis. This lets your LlamaIndex agent answer natural language questions about your Chewy keyword performance with answers backed by live data. The MCP Server makes it easy to feed these Chewy keywords into a LlamaIndex `FunctionAgent` for automated bidding analysis. Your LlamaIndex agent uses the indexed Chewy keyword performance to recommend optimizations based on actual search volume.

Setup guide

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

Your LlamaIndex pipeline takes the JSON outputs from tools like `get_campaign_details` and converts them into Document objects. These are then embedded and stored in your vector database for semantic search.

Yes, you can register the tools using `McpToolSpec` and pass them to a `FunctionAgent`. The engine will break down complex queries and call `list_chewy_campaigns` to answer specific sub-questions.

Yes, you can use `to_tool_list_async()` to load the tools into your LlamaIndex environment. This keeps your data retrieval loops fast when fetching heavy performance reports.

Install the required MCP tools package and initialize the client with your Vinkius endpoint URL. Pass the resulting tool list to your agent constructor to start querying your retail campaigns.

Every request fetching your product catalogs or campaign lists runs inside a zero-trust, ephemeral V8 isolate. Your raw Chewy account data is never cached or stored on the Vinkius platform, ensuring strict data privacy.

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