How to Use the Klevu (E-commerce AI Search) MCP in Pydantic AI

Q: How do I integrate Klevu (E-commerce AI Search) with Pydantic AI?

Install pydantic-ai-slimmcp. Create an MCPToolset pointing to your HTTP endpoint and add it to your Agent's toolsets. The framework handles the rest.

Q: Can Pydantic AI validate Klevu (E-commerce AI Search) autocomplete results?

Yes. When the agent calls searchautocomplete, the framework checks every suggested term against your Pydantic models. Bad suggestions trigger an immediate validation error.

Q: How does Klevu (E-commerce AI Search) handle pagination in Pydantic AI?

The agent uses searchpagination to step through large result sets. Each page of products undergoes the same strict type checking before the agent processes it.

Connect Klevu to Pydantic AI to enforce strict type validation on every catalog search and product recommendation.

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Connect Klevu (E-commerce AI Search) MCP to Pydantic AI

Create your Vinkius account to connect Klevu (E-commerce AI Search) to Pydantic AI 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 Klevu (E-commerce AI Search) with Pydantic AI

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Execute type-safe catalog queries

E-commerce APIs return messy data. When you attach this MCP Server to Pydantic AI, every single product returned by `search_keyword` gets validated against your predefined models. If Klevu returns an unexpected price format, the agent fails loudly instead of passing bad data to the frontend. This strict validation applies across the board. Whether your agent pulls entire departments with `search_category` or applies specific constraints with `search_filtered`, Pydantic AI guarantees the output matches your schema exactly. You never have to worry about silent corruption in your product grids.

Validate recommendations with Pydantic AI MCP Server

AI product suggestions only work if the data is reliable. Your agent calls `search_recs` to fetch related items, and the framework ensures every returned SKU has the required attributes. You get dynamic discovery backed by strict runtime checks. The same applies to global data. When you run `search_trending` to find popular products, Pydantic AI verifies the payload. If a trending item is missing an image URL required by your model, the system catches it immediately.

Manage raw payloads and complex sorting

Sometimes you need to bypass standard filters. The `search_raw` tool lets you send custom JSON directly to the Klevu backend. Your agent constructs the payload, and Pydantic AI validates the response structure from these MCP tools. Sorting operations require precision. The `search_sorted` tool handles custom ordering, while `search_product_id` pulls exact matches for specific items. Every operation is model-agnostic, meaning you swap Anthropic for local models without rewriting your validation logic.

Setup guide

Set up Klevu (E-commerce AI Search) MCP in Pydantic AI

Prerequisites

Python 3.10+ installed
pydantic-ai-slim[fastmcp] package
Active Vinkius subscription with a valid endpoint token

1

Install Pydantic AI with FastMCP
Run pip install "pydantic-ai-slim[fastmcp]". The FastMCP toolset replaces the deprecated MCPServerHTTP class with full protocol support.
2

Configure the FastMCPToolset
Pass a JSON-style config dict to FastMCPToolset with your Vinkius URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports Streamable HTTP, SSE, and Stdio transports.
3

Create and run your agent
Pass the toolset to Agent(toolsets=[toolset]) and call agent.run(). Swap openai:gpt-4o for any supported model — Anthropic, Google, Mistral, or Groq.

agent.py

from pydantic_ai import Agent
from pydantic_ai.toolsets.fastmcp import FastMCPToolset

toolset = FastMCPToolset({
    "mcpServers": {
        "klevu-e-commerce-ai-search-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

agent = Agent(
    "openai:gpt-4o",
    toolsets=[toolset],
    system_prompt="You have access to Klevu (E-commerce AI Search) tools.",
)

result = await agent.run("List recent Klevu (E-commerce AI Search) transactions")
print(result.output)

Get your connection token →

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Klevu. 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 Klevu (E-commerce AI Search) MCP in Pydantic AI

Install `pydantic-ai-slim[mcp]`. Create an `MCPToolset` pointing to your HTTP endpoint and add it to your Agent's toolsets. The framework handles the rest.

Yes. When the agent calls `search_autocomplete`, the framework checks every suggested term against your Pydantic models. Bad suggestions trigger an immediate validation error.

The agent uses `search_pagination` to step through large result sets. Each page of products undergoes the same strict type checking before the agent processes it.

Yes. The framework is completely model-agnostic. As long as the model supports tool calling, it interacts with the MCP Server and validates the outputs.

The MCP connection handles search strings, category IDs, and filter definitions. We run the integration inside an isolated V8 sandbox that requires a single endpoint token and wipes all state the moment the request completes.

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