How to Use the Zenserp MCP in Pydantic AI

Q: Can I validate multiple search engines using Zenserp and Pydantic AI?

Yes. You can call searchbing, searchduckduckgo, or searchgoogle tools, and the validation layer ensures that regardless of which engine you hit, the output structure is consistent for your code.

Q: Does Zenserp support varied search types with Pydantic AI?

It does. You can use searchimages, searchmaps, and searchshopping. The framework then validates the specific attributes—like coordinates or product UPCs—against your defined schemas.

Ensure every Zenserp search result is validated against your Pydantic AI models for guaranteed correctness in production code.

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Connect Zenserp MCP to Pydantic AI

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Setup Zenserp with Pydantic AI

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Validate Multi-Engine Search Results

The `search_bing`, `search_duckduckgo`, and `search_google` tools pull organic search results. When using Pydantic AI, even if the API changes format, your agent fails loudly with a validation error—you never get corrupt data. This means you can trust that whatever Zenserp returns from any major engine will match the strict schema defined in your models.

Structure Local and Visual Data

You pull product prices with `search_shopping` or local data via `search_maps`. Pydantic AI ensures that when you call these tools, the resulting fields (like 'price' or 'rating') are exactly what your code expects. It’s about correctness. This framework makes sure the agent doesn't proceed based on hallucinated or malformed tool output.

Guarantee Media Data Structure

Whether you use `search_news` for articles, or pull video results from `search_youtube`, Pydantic AI validates the resulting fields. You get typed Python objects instead of raw JSON. This level of strictness is critical when building model-agnostic agents that must function flawlessly regardless of the underlying API changes.

Setup guide

Set up Zenserp 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": {
        "zenserp-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

result = await agent.run("List recent Zenserp transactions")
print(result.output)

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Common questions about Zenserp MCP in Pydantic AI

The server provides live search results, and Pydantic AI intercepts those results. It forces every single data point returned by Zenserp to conform to the model you defined before your agent uses it.

Yes. You can call `search_bing`, `search_duckduckgo`, or `search_google` tools, and the validation layer ensures that regardless of which engine you hit, the output structure is consistent for your code.

It does. You can use `search_images`, `search_maps`, and `search_shopping`. The framework then validates the specific attributes—like coordinates or product UPCs—against your defined schemas.

The main upside is reliability. If the search API sends unexpected data, your agent fails cleanly with a validation error rather than continuing with bad information or silently corrupting memory.

The server touches live web search results (SERP), local business metadata, and media links. Your agent will be working with a mix of structured text, geospatial coordinates, and timestamps.

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