How to Use the Wolai MCP in Pydantic AI

Q: Can I query Wolai databases using Pydantic AI?

Yes. After running querydatabase, the resulting records are immediately validated against your schema. This means you get clean, predictable data objects every single time—no guesswork.

Q: Does Pydantic AI support listing all Wolai pages?

It does. The listpages tool is available and the resulting list of page objects will conform to your specified Pydantic model, keeping everything consistent.

Guarantee Data Correctness: Automate Wolai with Pydantic AI.

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Connect Wolai MCP to Pydantic AI

Create your Vinkius account to connect Wolai 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 Wolai with Pydantic AI

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MCP Server: Writing Reliable Wolai Database Records

Need to make sure data is correct? The agent uses `create_database_row` and, crucially, validates the output against Pydantic models. If the API sends unexpected field types, your agent fails loudly—no silent corruption. It's about correctness over speed.

MCP Server: Managing Wolai Pages with Type Safety

When creating or reading pages, you get guaranteed structure. Calling `get_page` returns data that matches your defined Pydantic schema. This prevents downstream errors when building complex applications around Wolai content.

MCP Server: Full Visibility into Wolai Structure

The agent provides a full audit trail of your workspace. You can call `list_databases` to see all available data sources, or use `get_workspace_info` to get general system details—all with strict type checking.

Setup guide

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

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

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

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Common questions about Wolai MCP in Pydantic AI

You pass the MCP Server toolset into your Agent. Every time your agent calls a Wolai function, Pydantic validates the response data against your defined models at runtime. This means you always know the shape of the data.

This server touches user lists, database schemas, page blocks, and general workspace metadata. The power here is that regardless of where the data comes from in Wolai, your agent treats it with guaranteed structure.

Yes. After running `query_database`, the resulting records are immediately validated against your schema. This means you get clean, predictable data objects every single time—no guesswork.

It does. The `list_pages` tool is available and the resulting list of page objects will conform to your specified Pydantic model, keeping everything consistent.

The core strength is runtime validation. If a field comes back that shouldn't be there, or if the wrong type of value arrives, your agent immediately fails with a clear error message. It prevents bad data from entering your system.

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