How to Use the ValueSERP MCP in Pydantic AI

Q: Can Pydantic AI use ValueSERP for academic data?

Yes. The googlescholarsearch tool pulls abstracts and publication details, which can be validated against a structured model, ensuring you're working with clean metadata.

Q: What search tools are best for Pydantic AI to manage product data?

Use googleshoppingsearch. It returns fields like product name and price that fit perfectly into a predefined model, eliminating the risk of hallucinated or missing data points.

Q: Does ValueSERP support finding local businesses with Pydantic AI?

It does. The googleplacessearch tool requires you to specify a place name and location, giving your agent the precise inputs needed for structured output.

Q: What specific data types are available from ValueSERP when using Pydantic AI?

The server handles diverse outputs: text results (from googlesearch), image URLs, structured product lists, news articles, and academic abstracts.

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Connect ValueSERP MCP to Pydantic AI

Create your Vinkius account to connect ValueSERP 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 ValueSERP with Pydantic AI

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Guaranteed Data Structure with ValueSERP and Pydantic AI

The `custom_serp_search` tool provides results that can be mapped to a specific JSON schema, making the output predictable. If you define the expected fields—like product names or prices—your agent knows what to expect. This structural reliability is key when using tools like `google_shopping_search`, which returns defined fields for product name, price, and merchant links.

Structured Context from Specialized Searches

Need more than just a query string? The agent can use specialized tools. For example, `get_related_questions` pulls Q&As into an expected structure. Similarly, `google_scholar_search` delivers structured abstracts and publication details. If the API returns unexpected data from these searches, Pydantic validates it and fails loudly—no silent corruption.

Handling Multimedia and Location Data

The server can deliver complex data types. `google_image_search` provides a list of direct URLs. For location tracking, `google_places_search` requires specific inputs (place name and location) to return predictable geographical records. Even general searches (`google_search`) provide text that your agent forces into a defined Pydantic output model.

Setup guide

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

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

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

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Common questions about ValueSERP MCP in Pydantic AI

You define the expected schema for your search results. When you call a tool like `google_video_search`, the Pydantic framework validates that the returned JSON adheres strictly to what your agent expects.

Yes. The `google_scholar_search` tool pulls abstracts and publication details, which can be validated against a structured model, ensuring you're working with clean metadata.

Use `google_shopping_search`. It returns fields like product name and price that fit perfectly into a predefined model, eliminating the risk of hallucinated or missing data points.

It does. The `google_places_search` tool requires you to specify a place name and location, giving your agent the precise inputs needed for structured output.

The server handles diverse outputs: text results (from `google_search`), image URLs, structured product lists, news articles, and academic abstracts.

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