How to Use the Baserow MCP in Pydantic AI

Enforce strict data types in Pydantic AI when interacting with Baserow records via this MCP Server.

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Connect Baserow MCP to Pydantic AI

Create your Vinkius account to connect Baserow 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 Baserow with Pydantic AI

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Type-safe Baserow interactions in Pydantic AI

Define your data models and let the server validate every response from `get_row`. If the data doesn't match, the agent stops before it can cause trouble. This prevents the common issue of hallucinated fields or unexpected types. You get clean, validated data ready for your application logic.

Reliable data entry with Pydantic AI

Use `create_row` to push data into your tables with confidence. The framework checks your JSON objects against the schema provided by `list_fields`. If you provide a value that doesn't fit the column type, the agent fails immediately. You never have to worry about silent corruption in your database.

Synchronize your Pydantic AI agents

Run `list_rows` to fetch your data and map it to your models. You can handle the response as a structured object, making your code easier to maintain. Use `update_row` to modify records only when the data passes your Pydantic validation. It's the most secure way to keep your database in sync with your agent.

Setup guide

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

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

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

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Common questions about Baserow MCP in Pydantic AI

You get runtime validation for every database interaction. It stops your agent from sending malformed data to your tables, keeping your records clean.

The framework treats tool responses as validated objects. If an error occurs, it triggers an exception that you can handle in your agent's standard error path.

Yes, it supports both Streamable HTTP and SSE transports. You just need to provide the server URL to your toolset configuration.

Yes, once you pass the toolset to your agent, it detects all available tools. It can list databases and tables to map the schema on the fly.

The server uses a strict, ephemeral authentication layer. Your row content is never cached long-term, and every request is validated against your specific endpoint token.

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