Bring No Code
to Pydantic AI
Create your Vinkius account to connect Baserow to Pydantic AI and start using all 10 AI tools in minutes. Fully managed, enterprise secure, and ready to use without writing a single line of code. No hosting, no server setup — just connect and start using.
GDPR Free for SubscribersCompatible with every major AI agent and IDE
Gemini
What is the Baserow MCP Server?
Connect your Baserow databases to any AI agent and take full control of your data through natural conversation.
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
How it works
- Subscribe to this server
- Enter your Baserow Database Token
- Start managing your data from Claude, Cursor, or any MCP-compatible client
No more clicking through the Baserow UI to find a record or update a field value. Your AI acts as a dedicated database administrator.
Who is this for?
- Product Teams — quickly query project tracking databases, update task statuses and review sprint data
- Developers — discover table schemas, create records programmatically and manage database content via conversation
- Data Analysts — explore data schemas, run filtered queries and export row data for analysis
Built-in capabilities (10)
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
Provide the table ID and row ID. WARNING: this action is irreversible. Delete a row from a Baserow table
Field names are returned in user-readable format. Provide the table ID and row ID. Get a specific row from a Baserow table
Provide the table ID from list_tables. Get details for a specific Baserow table
Each database shows its ID, name, workspace and creation date. Use this to discover available databases before querying their tables. List all Baserow databases
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
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
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
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
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
Why Pydantic AI?
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.
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Full type safety: every MCP tool response is validated against Pydantic models, catching data inconsistencies before they reach your application
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Model-agnostic architecture. switch between OpenAI, Anthropic, or Gemini without changing your Baserow integration code
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Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors
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Dependency injection system cleanly separates your Baserow connection logic from agent behavior for testable, maintainable code
Baserow in Pydantic AI
Why run Baserow with Vinkius?
The Baserow connection runs on our fully managed, secure cloud infrastructure. We handle the hosting, maintenance, and security so you don't have to deal with servers or code. All 10 tools are ready to work instantly without any complex setup.
You stay in complete control of your data. Your AI only accesses the information you approve, keeping your sensitive passwords and private details completely safe. Plus, with automatic optimizations, your AI works faster and more efficiently.
* Every connection is hosted and maintained by Vinkius. We handle the security, updates, and infrastructure so you don't have to write code or manage servers. See our infrastructure
Over 4,000 integrations ready for AI agents
Explore a vast library of pre-built integrations, optimized and ready to deploy.
Connect securely in under 30 seconds
Generate tokens to authenticate and link external services in a single step.
Complete visibility into every agent action
Audit live requests, latency, success rates, and active security compliance policies.
Optimize spending and track token ROI
Analyze real-time token consumption and cost metrics detailed by connection.
Explore our live AI Agents Analytics dashboard to see it all working
This dashboard is included when you connect Baserow using Vinkius. You will never be left in the dark about what your AI agents are doing with your tools.
Baserow and 4,000+ other AI tools. No hosting, no code, ready to use.
Professionals who connect Baserow to Pydantic AI through Vinkius don't need to write code, manage servers, or worry about security. Everything is pre-configured, secure, and runs automatically in the background.
Raw MCP | Vinkius | |
|---|---|---|
| Ready-to-use MCPs | Find and configure each manually | 4,000+ MCPs ready to use |
| Connection Setup | Manual coding & server setup | 1-click instant connection |
| Server Hosting | You host it yourself (needs 24/7 uptime) | 100% hosted & managed by Vinkius |
| Security & Privacy | Stored in plaintext config files | Bank-grade encrypted vault |
| Activity Visibility | Blind execution (no logs or tracking) | Live dashboard with real-time logs |
| Cost Control | Runaway AI token spend risk | Automatic budget limits |
| Revoking Access | Must delete files or code to stop | 1-click disconnect button |
How Vinkius secures
Baserow for Pydantic AI
Every request between Pydantic AI and Baserow is protected by our secure gateway. We automatically keep your sensitive data private, prevent unauthorized access, and let you disconnect instantly at any time.
Frequently asked questions
How do I get a Baserow API Token?
Log in to your Baserow workspace, go to Database Settings > API Tokens (or Workspace Settings > API Tokens), click Create Token, give it a name and set the permissions (create, read, update, delete) for specific tables. Copy the token immediately — it won't be shown again.
Can I create and update rows programmatically?
Yes! Use create_row with the table ID and a JSON object of field_name: value pairs. Use update_row with the table ID, row ID and the fields to update. First use list_fields to discover the available field names and their types for the table.
Can I filter rows by field values?
Yes! The list_rows tool supports pagination and ordering. Use the page and size parameters for pagination, and order_by to sort by any field (prefix with - for descending). The underlying API also supports field-level filtering through the filter parameter.
What field types does Baserow support?
Baserow supports: text, number, boolean, date, long_text, email, url, single_select, multiple_select, link_row (relationships), file, rating, formula, lookup, rollup, last_modified and created_on fields. Use list_fields to see the exact types in your table.
How does Pydantic AI discover MCP tools?
Create an 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?
Yes. When you define result types as Pydantic models, every tool response is validated against the schema. Invalid data raises a clear error instead of silently corrupting your pipeline.
Can I switch LLM providers without changing MCP code?
Absolutely. Pydantic AI abstracts the model layer. your Baserow MCP integration works identically with OpenAI, Anthropic, Google, or any supported provider.
MCPServerHTTP not found
Update: pip install --upgrade pydantic-ai
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