Baserow MCP. Manage data structure and records with conversation.
Gemini Works with every AI agent you already use
…and any MCP-compatible client
Just plug in your AI agents and start using Vinkius.
Baserow MCP lets your AI agent act like a dedicated database administrator for no-code databases. You can discover all available tables and schemas, run filtered queries on rows of data, and modify records—creating or updating entire entries—all through natural conversation.
This means you never have to click through the Baserow UI again.
What your AI agents can do
Create row
Adds a brand new row into a table by filling out all the necessary field names and data points.
Delete row
Permanently deletes an entire row from a table. This action cannot be undone.
Get row
Pulls the full details for one single row if you know both its ID and the table it belongs to.
List every database and all the tables within it to understand your entire information architecture.
See a table's columns, including their type (text, number, date, etc.), before trying to write or read any specific value.
Query rows based on multiple conditions—like finding all tasks assigned to Alice that are overdue.
Build a brand new row in any table by providing the required field names and values.
Change specific fields within an existing record, like changing a task status from 'To Do' to 'In Progress'.
Ask AI about this MCP
Supported MCP Clients
OAuth 2.0 Compatible Gemini Waiting for input…
Baserow: 10 Tools for Database Management
These ten tools allow your AI client to perform every standard CRUD operation on Baserow databases, from discovering schemas to deleting individual rows.
Make your AI actually useful.
Add this MCP to Claude, Cursor, or Windsurf and your AI stops guessing. It gets real tools to look things up, take action, and handle the stuff you keep doing by hand.
Start using Baserow on Vinkius019d841ccreate row
Adds a brand new row into a table by filling out all the necessary field names and data points.
019d841cdelete row
Permanently deletes an entire row from a table. This action cannot be undone.
019d841cget row
Pulls the full details for one single row if you know both its ID and the table it belongs to.
019d841cget table
Retrieves detailed metadata for one specific table ID you've already discovered.
019d841clist databases
Lists every database available in your Baserow workspace so you know where to look first.
019d841clist fields
Examines a table to show every column name and its data type, preventing you from making schema errors.
019d841clist rows
Finds and lists rows in a table, allowing you to filter results by specific field values and pages.
019d841clist tables
Discovers all specific tables within a given database, showing their names and field counts.
019d841clist views
Shows the different saved views (like Kanban or Gallery) that exist for a table, along with their filter rules.
019d841cupdate row
Changes values in specific fields within an existing record, modifying only what you tell it to change.
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 4,800+ other servers whenever your AI needs more. One click, no limits.
- Use this MCP plus 4,800+ 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
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.
VINKIUS INFRASTRUCTURE
Cloud Hosted
Managed infra
V8 Isolated
Sandboxed per request
Zero-Trust Proxy
No stored credentials
DLP Enforced
Policy on every call
GDPR Compliant
EU data residency
Token Compression
~60% cost reduction
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.
The endless clicking through dashboards.
Right now, updating a single record means logging into the database portal, finding the correct table, navigating to the right row, and then manually changing the status field. If you need to do that ten times for twenty different people, you're spending hours just clicking around and copy-pasting data.
With this MCP, your agent handles all those clicks behind the scenes. You simply tell it, 'Set the priority of these five tasks to high.' The AI executes the necessary sequence of reads and updates instantly, giving you a clean confirmation that everything is done.
The Baserow MCP gives you full control over data structure.
You eliminate manual schema checks. Before attempting to create a record, your agent can first run `list_fields` to confirm the field names and required types. This prevents errors that stop your workflow dead in its tracks.
It’s pure control. You treat the entire database like an extension of your conversation—a direct, conversational data administrator.
What you can do with this MCP connector
You connect your internal database structures directly to any AI agent and take full control of your data using just plain language. Instead of manually clicking into Baserow, navigating tabs, and copy-pasting field values, your AI client handles all the dirty work. You can ask it to find specific records or update status fields across multiple tables instantly.
This works because you're not dealing with raw API calls; you're using a structured interface that understands what data exists where. When you run complex tasks, know that every action is tracked and visible via Vinkius AI Analytics, giving you full visibility into exactly which tools were called and what data flowed through.
This makes managing sensitive project tracking databases safer than ever.
019d841c-c442-73eb-ba73-aa3727629df9 
How Baserow MCP Works
- 1 Subscribe to this MCP and provide your Baserow Database Token.
- 2 Connect the service to your preferred AI client (like Cursor or Claude).
- 3 Tell your agent what you need done—for example, 'Find all project records where the status is delayed'—and watch it execute the necessary steps.
The bottom line is that your database management becomes a conversation with your AI client, not a series of clicks on a dashboard.
Who Is Baserow MCP For?
Product teams and operations managers who are done clicking through dashboards to update status fields. Developers who need to programmatically manage content without writing full API code. Data analysts needing quick, structured access to raw data.
Needs to quickly find all task records that are past due and update their status in bulk.
Requires creating new test records or updating metadata fields programmatically for testing purposes.
Needs to run complex, filtered queries across multiple tables and export the resulting data set for a spreadsheet.
What Changes When You Connect
- Stop guessing field names. Using
list_fieldsensures your agent knows the exact column name, so you never run into an 'unknown parameter' error when callingcreate_roworupdate_row. - Need to find a specific project status? Instead of opening the UI and applying filters, just ask your agent. It uses
list_rowsto query records with pagination and ordering automatically. - Never delete data by accident again. The MCP gives you granular control: use
delete_rowonly when absolutely sure, and always verify the ID first. - You don't need to memorize table names. Start by running
list_databases, then follow up withlist_tablesto map out your entire data landscape before doing anything else. - The power is in discovery. You can use
list_viewsto understand how the team has already organized the data, making sure you don't overwrite a critical reporting structure.
Real-World Use Cases
Updating project status for a sprint review
A product manager needs to mark 15 tasks as 'Complete'. Instead of opening the Tasks table and updating 15 rows manually, they ask their agent. The agent uses list_rows to find all relevant IDs and then cycles through them using update_row, saving hours of clicking.
Onboarding a new client record
A sales rep needs to create an entire client profile. They ask their agent, which uses list_fields first to confirm the schema, then calls create_row, filling out name, contact info, and project scope all in one go.
Analyzing old data for a quarterly report
A data analyst needs records from 2023. They ask their agent to run a filtered query using list_rows, specifying the date field and exporting the resulting rows, skipping manual date range filters.
Debugging missing content
A developer is debugging why a specific record isn't showing up. They ask their agent to first run get_table to verify the table ID, and then use get_row with the suspected ID to confirm if the data physically exists.
The Tradeoffs
Assuming field names are correct
Trying to update a record by inputting 'Project Date' when the actual schema field is named 'start_date'. This fails and wastes time.
→
Always run list_fields first. Use the exact field name returned by that function in your agent prompt, especially before calling update_row.
Overwriting data without checking
Telling the agent to 'set status to complete' on a whole table view, which might actually update unrelated records.
→
Limit your scope. Always include filtering instructions in your query (using list_rows) and specify the exact record ID you want modified when using update_row.
Bypassing discovery tools
Assuming that because a column exists in the UI, the agent knows its proper data type or if it needs to be linked via a foreign key.
→
Run list_fields before any modification. It tells you the required format (text, number, select) for reliable calls.
When It Fits, When It Doesn't
Use this MCP if your data lives in structured tables and requires full CRUD operations. You're managing records that have defined columns, like project tasks or inventory items. Don't use it if you just need to store a simple message thread; for that, a messaging-focused MCP is better. If your primary goal is only reading data without modification, list_rows might suffice, but using the full suite of tools gives you safety and control over everything from schema discovery (list_fields) to making changes (update_row).
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 data structures using `list_databases` and `list_tables`? +
You first run list_databases to see every workspace available. Then, use the resulting IDs with list_tables to map out the exact structure of your data before writing any queries.
What information does the `list_views` tool provide about my data? +
The list_views tool shows how different sets of data are grouped and filtered within Baserow. This lets your agent understand not just what data exists, but how it's commonly organized or viewed by users.
When should I use the `delete_row` function, and am I warned about permanence? +
The delete_row tool permanently removes records from a table. Because this action is irreversible, always confirm the Table ID and Row ID with your agent before authorizing any deletion.
If I only know the record's ID, how do I quickly retrieve its data using `get_row`? +
Use get_row when you have the specific Table ID and Row ID. This bypasses listing entire tables or running complex queries, giving you direct access to that single record's data.
Use it with your favorite AI tools
Connect this server to Cursor, Claude, VS Code, and more.