Baserow MCP Server for Pydantic AI 10 tools — connect in under 2 minutes
Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect Baserow through Vinkius and every tool is automatically validated against Pydantic schemas. catch errors at build time, not in production.
ASK AI ABOUT THIS MCP SERVER
Vinkius supports streamable HTTP and SSE.
import asyncio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerHTTP
async def main():
# Your Vinkius token. get it at cloud.vinkius.com
server = MCPServerHTTP(url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
agent = Agent(
model="openai:gpt-4o",
mcp_servers=[server],
system_prompt=(
"You are an assistant with access to Baserow "
"(10 tools)."
),
)
result = await agent.run(
"What tools are available in Baserow?"
)
print(result.data)
asyncio.run(main())
* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure
About Baserow MCP Server
Connect your Baserow databases to any AI agent and take full control of your data through natural conversation.
Pydantic AI validates every Baserow tool response against typed schemas, catching data inconsistencies at build time. Connect 10 tools through Vinkius and switch between OpenAI, Anthropic, or Gemini without changing your integration code. full type safety, structured output guarantees, and dependency injection for testable agents.
What you can do
- Database Discovery — List all databases and tables the token has access to with their schemas
- Schema Exploration — Browse table fields (columns) with their types (text, number, boolean, date, select, etc.)
- Row Operations — List, create, update and delete rows with full CRUD support
- Filtered Queries — Query rows with pagination, ordering and field-based filtering
- View Management — List configured views (grid, gallery, kanban, form, calendar) with their filter and sort rules
The Baserow MCP Server exposes 10 tools through the Vinkius. Connect it to Pydantic AI in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.
How to Connect Baserow to Pydantic AI via MCP
Follow these steps to integrate the Baserow MCP Server with Pydantic AI.
Install Pydantic AI
Run pip install pydantic-ai
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token
Run the agent
Save to agent.py and run: python agent.py
Explore tools
The agent discovers 10 tools from Baserow with type-safe schemas
Why Use Pydantic AI with the Baserow MCP Server
Pydantic AI provides unique advantages when paired with Baserow through the Model Context Protocol.
Full type safety: every MCP tool response is validated against Pydantic models, catching data inconsistencies before they reach your application
Model-agnostic architecture. switch between OpenAI, Anthropic, or Gemini without changing your Baserow integration code
Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors
Dependency injection system cleanly separates your Baserow connection logic from agent behavior for testable, maintainable code
Baserow + Pydantic AI Use Cases
Practical scenarios where Pydantic AI combined with the Baserow MCP Server delivers measurable value.
Type-safe data pipelines: query Baserow with guaranteed response schemas, feeding validated data into downstream processing
API orchestration: chain multiple Baserow tool calls with Pydantic validation at each step to ensure data integrity end-to-end
Production monitoring: build validated alert agents that query Baserow and output structured, schema-compliant notifications
Testing and QA: use Pydantic AI's dependency injection to mock Baserow responses and write comprehensive agent tests
Baserow MCP Tools for Pydantic AI (10)
These 10 tools become available when you connect Baserow to Pydantic AI via MCP:
create_row
Requires the table ID and a JSON object with field_name: value pairs matching the table schema. Use list_fields to discover available field names. Returns the created row with its ID and all field values. Create a new row in a Baserow table
delete_row
Provide the table ID and row ID. WARNING: this action is irreversible. Delete a row from a Baserow table
get_row
Field names are returned in user-readable format. Provide the table ID and row ID. Get a specific row from a Baserow table
get_table
Provide the table ID from list_tables. Get details for a specific Baserow table
list_databases
Each database shows its ID, name, workspace and creation date. Use this to discover available databases before querying their tables. List all Baserow databases
list_fields
Each field shows its ID, name, type (text, number, boolean, date, single_select, long_text, link_row, file, etc.), order and required status. Use this to understand the data schema before querying or creating rows. List fields (columns) of a Baserow table
list_rows
Optionally filter by field values (using user_field_names) and set page/size for pagination. Results include count, next/previous page URLs and the rows array. Use field names (not IDs) for readable results. List rows in a Baserow table
list_tables
Each table shows its ID, name, database, field count and creation date. Use this to discover the data schema before querying rows. List all tables accessible in Baserow
list_views
Each view shows its ID, name, type, filter settings and sort rules. Useful for understanding how data is organized and filtered in the UI. List views configured for a Baserow table
update_row
Requires the table ID, row ID and a JSON object with field_name: value pairs for the fields to update. Only provided fields will be modified. Use list_fields to discover available field names. Update an existing row in a Baserow table
Example Prompts for Baserow in Pydantic AI
Ready-to-use prompts you can give your Pydantic AI agent to start working with Baserow immediately.
"List all tables in my Baserow workspace."
"Show me all rows in the Tasks table where Status is 'In Progress'."
"Create a new task called 'Review PR #234' assigned to Alice with status 'To Do'."
Troubleshooting Baserow MCP Server with Pydantic AI
Common issues when connecting Baserow to Pydantic AI through the Vinkius, and how to resolve them.
MCPServerHTTP not found
pip install --upgrade pydantic-aiBaserow + Pydantic AI FAQ
Common questions about integrating Baserow MCP Server with Pydantic AI.
How does Pydantic AI discover MCP tools?
MCPServerHTTP instance with the server URL. Pydantic AI connects, discovers all tools, and generates typed Python interfaces automatically.Does Pydantic AI validate MCP tool responses?
Can I switch LLM providers without changing MCP code?
Connect Baserow with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
Anthropic's native desktop app for Claude with built-in MCP support.
AI-first code editor with integrated LLM-powered coding assistance.
GitHub Copilot in VS Code with Agent mode and MCP support.
Purpose-built IDE for agentic AI coding workflows.
Autonomous AI coding agent that runs inside VS Code.
Anthropic's agentic CLI for terminal-first development.
Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
Type-safe agent development for Python with first-class MCP support.
TypeScript toolkit for building AI-powered web applications.
TypeScript-native agent framework for modern web stacks.
Python framework for orchestrating collaborative AI agent crews.
Leading Python framework for composable LLM applications.
Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
Connect Baserow to Pydantic AI
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.