Baserow MCP. Manage databases and schemas via conversation.
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Baserow MCP Server connects your AI agent to your Baserow databases. You can list databases and tables, discover schemas using `list_fields`, and perform full CRUD operations (create, read, update, delete) on rows via natural conversation.
Use it to manage structured data without touching the Baserow UI.
What your AI agents can do
Create row
Creates a new row in a Baserow table using field name: value pairs.
Delete row
Deletes an entire row from a Baserow table. This action cannot be undone.
Get row
Retrieves the full data for a specific row using its ID.
The agent lists all connected Baserow databases and the tables within them, giving you a map of the available data.
You can list the fields (columns) for any table, seeing their names, types (text, date, number, etc.), and whether they're required.
The agent searches and retrieves specific rows, supporting field-based filtering, sorting, and pagination.
You instruct the agent to make changes—creating new records or updating field values in existing rows.
The agent lists configured data views for a table, detailing their specific filter and sort rules.
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Supported MCP Clients
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Baserow MCP Server: 10 Tools for Data Management
These tools allow your AI agent to perform every core database operation—from discovering schemas to managing records—directly through conversation.
019d841ccreate row
Creates a new row in a Baserow table using field name: value pairs.
019d841cdelete row
Deletes an entire row from a Baserow table. This action cannot be undone.
019d841cget row
Retrieves the full data for a specific row using its ID.
019d841cget table
Gets detailed metadata for a specific Baserow table.
019d841clist databases
Lists all connected Baserow databases, providing IDs, names, and creation dates.
019d841clist fields
Lists all columns (fields) of a Baserow table, including their types and required status.
019d841clist rows
Lists multiple rows in a Baserow table, supporting field filtering, sorting, and pagination.
019d841clist tables
Lists all accessible tables across the entire Baserow workspace.
019d841clist views
Lists all configured data views (Kanban, Gallery, etc.) for a specific table.
019d841cupdate row
Modifies the field values in an existing row using its ID.
Choose How to Get Started
Build a custom MCP for your own tools, or connect a ready-made integration from our catalog.
Build Your Own
Turn any API into an MCP. Import a spec, define Agent Skills, or deploy with MCPFusion.
- Import from OpenAPI, Swagger, or YAML specs
- Create Agent Skills with progressive disclosure
- Deploy to edge with MCPFusion framework
- Built in DLP, auth, and compliance on every call
- Real time usage dashboard and cost metering
- Publish to catalog or keep private
Make Your AI Do More
Start with Baserow, then connect any of our 4,700+ other servers whenever your AI needs more. One click, no limits.
- Use this MCP plus 4,700+ others, all in one place
- Add new capabilities to your AI anytime you want
- Every connection is secured and compliant automatically
- Track usage and costs across all your servers
- Works with Claude, ChatGPT, Cursor, and more
- New servers added to the catalog every week
What you can do with this MCP connector
Baserow MCP Server hooks up your AI agent to your Baserow databases. You get total control over your structured data just by talking to it. Your agent acts like a dedicated database admin, letting you manage tables, schemas, and records without opening the Baserow UI.
Listing Data and Structure
Your agent first gives you a map of what's available. You can use list_databases to see all connected Baserow databases, getting their IDs, names, and when they were created. Next, list_tables shows you every accessible table across the whole workspace. You can then get detailed metadata for a specific table using get_table.
To understand what fields a table has, the agent runs list_fields, showing you every column's name, its data type, and if it's required. Finally, you can see all configured data views (like Kanban or Gallery) for a table by running list_views, which details their specific filter and sort rules.
Working with Rows and Data
When you need data, the agent runs list_rows, letting you search and pull multiple rows from a table. This tool supports complex filtering, sorting, and pagination. You can pinpoint data by getting the full details of a single row using get_row with just its ID. If you need to change a row, you use update_row to modify field values in an existing record by its ID.
To build new data, you just tell the agent to create a row, and it runs create_row using field name: value pairs. Remember that delete_row takes out an entire row from a table, and that action can't be undone.
How You Use It
Your AI client uses these exposed tools to handle everything. You tell your agent exactly what you want—say, 'Show me all tasks assigned to Bob due this week'—and it executes the necessary database calls. You can then make changes, like 'Update John's status to Complete' or 'Create a new entry for the Q3 meeting'—and the agent handles the dirty work.
You manage your structured data flow entirely through conversation, without ever touching a mouse.
How Baserow MCP Works
- 1 Subscribe to the server and provide your Baserow Database Token.
- 2 Your AI client calls
list_databasesandlist_tablesto map the available data structure. - 3 You issue a command (e.g., 'Update the status of the 'Project Alpha' row to Complete') and the agent executes
update_row.
The bottom line is, your AI client treats your Baserow workspace like a programmatic database, not a graphical user interface.
Who Is Baserow MCP For?
The data analyst who needs to run ad-hoc reports without filing a ticket. The product manager who needs to update sprint statuses immediately. The developer who needs to prototype data workflows without writing boilerplate API calls. This is for anyone who spends too much time clicking between tabs to find a single piece of information.
Runs filtered queries and exports row data for deep analysis, bypassing the need to manually filter and copy data in the web UI.
Updates task statuses or project records instantly. Instead of finding the right row, they just tell their agent to 'Change status to Done' for a specific item.
Discovers table schemas and creates records programmatically. They use the agent to validate data structure before writing any code.
What Changes When You Connect
- Structured Data Discovery: Use
list_tablesandlist_fieldsto map out your entire data model. You don't have to guess what fields exist; the agent shows you the schema. - Complex Data Retrieval: Instead of simple search bars, use
list_rowsto query data. You specify filters, pagination, and sorting rules in natural language, getting structured results every time. - Instant Status Updates: Need to change a task status? Use
update_row. You tell the agent the row ID and the new value, and it executes the change instantly. - Safe Data Changes: Before you write anything, use
list_fieldsto confirm the field name and type. This prevents you from making calls that fail because you misspelled a column name. - Full Data Lifecycle Control: With
create_row,update_row, anddelete_row, you gain full programmatic control. Your agent handles the full data lifecycle, from initial creation to archival. - View Management: Use
list_viewsto understand how the data is organized. You see the configured filters and sort rules applied to specific views without having to check the Baserow UI.
Real-World Use Cases
Reviewing Project Status for a Sprint Review
The PM needs to see all tasks in the 'Sprint 5' table that are 'Blocked' or 'Needs Review'. Instead of manually setting filters on the Baserow UI, they ask their agent. The agent uses list_rows with complex filtering, retrieving only the relevant data points immediately.
Onboarding a New Developer to the Schema
A new developer needs to know how the 'User Profiles' table is structured. They ask their agent to run list_fields. The agent returns the schema (text, number, date, etc.) instantly, allowing the developer to start building queries without guesswork.
Bulk Updating Project Milestones
The Project Coordinator needs to mark 15 specific project milestones as 'Complete' and log the completion date. They list the target row IDs and ask the agent to update_row for all of them, handling the date field and status change in one go.
Auditing Data Integrity Before Export
A data analyst needs to confirm that a specific record exists and hasn't been touched. They run get_row using the known row ID. This confirms the current state of the record before they export the data for external analysis.
The Tradeoffs
Treating the AI like a Search Bar
User asks: 'Show me all records about projects.' The agent fails because the tool needs specific table IDs and structured queries, not a vague natural language search.
→
First, run list_databases to find the correct database. Then, use list_tables to pick the correct table ID. Finally, use list_rows and specify the field name and required value to narrow the results.
Assuming Field Names
User tries to update a field: 'Set the progress to 80%'. The agent fails because the user didn't know the exact field name (e.g., 'progress_percent').
→
Always run list_fields first. This shows you the exact field name (e.g., progress_percent) and its type, ensuring your update_row call uses the correct syntax.
Ignoring Data Dependencies
User tries to delete a row, but the system fails because other tables reference that row ID. The user doesn't know the dependencies.
→
Before deleting, use get_table to check the table's structure and list_fields to see if any fields are defined as link_row types. This helps you understand data dependencies before calling delete_row.
When It Fits, When It Doesn't
Use this server if your workflow requires treating your Baserow data like a backend API, not a dashboard. You need to programmatically check schemas (list_fields), bulk update status fields (update_row), or run complex, filtered queries (list_rows) without manual UI interaction. Don't use this if you just need to view a single, static page or if your data source is a file upload (like CSV). If your goal is simply data consumption, the list_rows tool is your primary mechanism. If your goal is structural understanding, start with list_databases and list_tables.
Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Baserow. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.
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Works with Claude, ChatGPT, Cursor, and more
The Model Context Protocol standardizes how applications expose capabilities to LLMs. Instead of operating in isolation, your AI gains direct access to external platforms, live data, and real-world actions through secure, standardized connections.
This server provides 10 capabilities that interface natively with Claude, ChatGPT, Cursor, and any MCP client. No middleware. No custom integration required.
Available Capabilities
Finding a specific record in a no-code database shouldn't require 15 clicks.
Today, finding a single record means navigating to the correct database, selecting the right table, filtering by one field, then adding a second filter, checking the view settings, and finally copying the data out. It’s a sequence of clicks and context switches that burns time.
With the Baserow MCP Server, you just tell your agent what you need. 'Get the row for Project Phoenix, assigned to Sarah.' The agent executes the necessary API calls, and you get the clean, structured data right back. No clicks needed.
Baserow MCP Server: Full Data Control
You eliminate the need to use the Baserow UI for basic operations. There's no more manually changing a status field or deleting a row from the grid view. You simply command the change, and the agent executes it.
Your AI client now talks directly to the database layer. It treats your data like a true backend resource, giving you the power to manage records and schemas programmatically.
Common Questions About Baserow MCP
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 do I discover all available tables using the `list_tables` tool? +
You call list_tables to see every table in your Baserow workspace. This call returns a list, showing the table ID, name, database, field count, and creation date for all accessible tables.
What information does the `list_fields` tool provide about a specific table? +
The list_fields tool provides the schema for a table. It lists every field (column) along with its ID, name, data type (like text, number, or date), order, and whether it's required.
Can the `create_row` tool handle complex data types like link rows or files? +
Yes, create_row accepts data for complex types. You must use list_fields first to confirm the exact field names and format required for the JSON payload.
Does the `get_row` tool return all the data in a row? +
Yes, get_row retrieves the entire row's contents. It returns the row ID and all associated field values, provided you supply the correct table ID and row ID.
Use it with your favorite AI tools
Connect this server to Cursor, Claude, VS Code, and more.
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