Baserow MCP. Control your no-code database from chat.
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 Server connects your AI agent directly to your no-code relational database. Manage structured data, list workspaces, and orchestrate complex workflows by programmatically reading, creating, updating, and deleting records.
It acts as a dedicated data architect for your entire Baserow backend.
What your AI agents can do
Create row
Creates a new row in a table by providing data as a JSON string of field names and values.
Delete row
Removes an entire row of data from a specified table.
Get row
Retrieves all the specific details for one row using its unique identifier.
Retrieves a list of every workspace you have set up in your Baserow account.
Retrieves a list of all operational databases (applications) within your Baserow account.
Shows all the individual tables contained within a specific database application.
Retrieves the names and types of all columns (fields) in a specified table.
Searches and retrieves multiple records from a table, supporting pagination and filters.
Adds a new row of data to a specified table using a JSON structure of field names and values.
Modifies the content of a specific row, allowing you to change field values in place.
Ask AI about this MCP
Supported MCP Clients
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Baserow MCP Server: 9 Tools for Data Management
These tools give your AI agent the power to interact with every aspect of your Baserow backend, from listing workspaces to managing individual data records.
019dd0c0create row
Creates a new row in a table by providing data as a JSON string of field names and values.
019dd0c0delete row
Removes an entire row of data from a specified table.
019dd0c0get row
Retrieves all the specific details for one row using its unique identifier.
019dd0c0list applications
Lists all the major database applications available in your Baserow workspace.
019dd0c0list fields
Shows the names and types of all columns (fields) within a given table.
019dd0c0list rows
Searches and lists multiple rows from a table, supporting filtering and pagination.
019dd0c0list tables
Lists all the individual tables contained within a selected database application.
019dd0c0list workspaces
Retrieves a list of every workspace container you have set up.
019dd0c0update row
Changes the data in an existing row by providing a JSON structure containing the field names and new values.
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
Your AI agent connects straight to your Baserow no-code database. It lets you handle structured data and build complex workflows by programmatically reading, creating, updating, and deleting records. It's your dedicated data architect for the whole Baserow backend.
To start, your AI agent can list every workspace you've got set up. It'll show all the operational databases (applications) within your account. You can then list every individual table inside a specific database application. From there, it'll retrieve the names and types of all columns (fields) in a table.
The agent can search and pull multiple records from a table, supporting filters and pagination. If you need to build data, it can create a new row in a table using a JSON structure of field names and values. It'll also let you modify the content of an existing row, letting you change field values right where they are.
When you're done with a record, the agent can remove the entire row of data from a specified table. You can also search for a single row and get all its specific details using its unique identifier. You can finally list and search multiple rows from a table, supporting filters and pagination.
How Baserow MCP Works
- 1 Get your Database Token from your Baserow settings.
- 2 Connect your preferred AI client (Claude, Cursor, etc.) to the Baserow MCP Server.
- 3 Ask your agent to perform an action, like 'Find all project records marked 'In Review' in the Project Tracker database.'
The bottom line is, your AI agent handles the connection and the logic, letting you manage structured data entirely through conversation.
Who Is Baserow MCP For?
This is for anyone who uses structured data but hates manual database interaction. Think project managers who need status updates across multiple project boards, or data analysts who need to automate data collection without leaving their chat window. If your job involves keeping track of records in a spreadsheet-like tool, you need this.
Uses the agent to pull project records and update statuses across multiple tables using natural language commands.
Automates collecting structured information and managing relational tables by instructing the agent, eliminating the need to manually build complex queries.
Integrates no-code backends into custom business workflows by having the agent query or write data using simple, structured queries.
What Changes When You Connect
- Automate Status Updates: Instead of opening a sheet, finding the right record, and manually changing a status dropdown, just ask the agent to 'Update the status for Project X to Complete.' The
update_rowtool handles the whole transaction. - Deep Data Discovery: Don't guess what data exists. Use
list_fieldsto see every column in a table, orlist_tablesto map out the entire database structure before you even write a query. - Zero-Click Schema Mapping: Need to know what data is available? Use
list_applicationsto see all your databases, thenlist_tablesto map the structure without clicking through menus. - Targeted Record Retrieval: Forget scrolling through thousands of rows. The
list_rowstool lets you search and paginate results, pulling exactly the records you need with semantic keywords. - Build Data Pipelines: By chaining tools—like
list_workspacesto find a project, thenlist_tablesto find the project roster, and finallyget_row—you build complex data workflows in a single conversation. - Data Integrity Checks: The server allows your agent to verify account-level permissions and retrieve token metadata, giving you instant visibility into your data infrastructure's health.
Real-World Use Cases
Quarterly Project Audit
The PM needs to know which projects are stalled. They ask their agent to search for records across all databases where the 'Last Status Update' field is older than 60 days. The agent uses list_applications to scope the search and list_rows to pull the necessary data into a single report.
Onboarding New Team Members
A manager needs to create a new user profile and assign them to a project. They ask the agent to create a new row in the 'Users' table, specifying all required fields (name, email, start date). The agent uses the create_row tool, ensuring all required fields are populated immediately.
Data Syncing After a Merge
The data team has merged two separate departments and needs to ensure all legacy records are accounted for. They ask the agent to list all tables in the main database, identify the old tables, and then use list_tables and list_fields to compare schema parity before moving data.
Updating Client Information in Bulk
A sales rep finds a client's record but realizes their phone number is wrong. They ask the agent to update the specific row ID for the client's record. The agent uses update_row to modify the phone number field, leaving all other data intact.
The Tradeoffs
Manual JSON Construction
Writing the agent prompt: 'Please create a row. The data is: name=John, status=active, project_id=123, etc.' This is brittle and fails if the field order changes.
→
Instead, just tell the agent: 'Create a new user row for John Doe, setting the status to active and linking it to Project 123.' The agent uses create_row and formats the JSON data correctly for you.
Forgetting to Search First
Trying to update a row by simply telling the agent, 'Update the row for John Doe.' The agent can't find the specific record ID without a search first.
→
First, ask the agent to list_rows and search by 'John Doe' to get the correct Row ID. Then, issue the update_row command with that specific ID.
Confusing Data Structure
Trying to write a custom query that joins multiple tables manually. This is difficult and prone to errors.
→
Use the agent to list_tables and list_fields first. Understanding the schema makes the agent's subsequent queries (like list_rows or get_row) precise and reliable.
When It Fits, When It Doesn't
Use this server if your workflow involves reading, writing, or inspecting structured data that lives in a no-code database like Baserow. You need to automate the 'find data' $\rightarrow$ 'read data' $\rightarrow$ 'change data' cycle without opening the web UI. The get_row, list_rows, and update_row tools are your core operational tools. Don't use this if your goal is just to view a single record—just open the app. Also, don't use this for complex, graph-like relationship traversal (e.g., 'who knows who'). You'll need a dedicated graph database tool for that. This server is best for structured, relational, record-based management.
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 9 capabilities that interface natively with Claude, ChatGPT, Cursor, and any MCP client. No middleware. No custom integration required.
Available Capabilities
Manually building reports from spreadsheets is tedious.
Today, if you need to pull a report, you have to open the spreadsheet, click the correct tab, filter by status, manually export the data, and then copy-paste it into a separate document. It takes 15 clicks, and you're always fighting with stale permissions or confusing views.
With the Baserow MCP Server, you just tell your agent what you want. It uses tools like `list_applications` and `list_rows` to find the data, pull the relevant fields, and give you the clean result—no clicks, no exporting, just the data you need.
Baserow MCP Server: Update data in chat.
Forget navigating to the record, finding the field, and hitting save. You can now instruct your agent: 'Update the status for the XYZ project to 'Done'.'. The agent executes `update_row` instantly, handling the unique IDs and data formatting for you.
It's immediate, conversational data management. You manage the backend without ever leaving your chat interface.
Common Questions About Baserow MCP
How do I use the `list_applications` tool with Baserow MCP Server? +
The list_applications tool shows you every database container you have. You run this first to see what databases are available to work with.
Can I use `list_rows` to search for specific data in Baserow MCP Server? +
Yes. list_rows supports semantic searching and pagination. You just tell the agent what you're looking for, and it handles the filtering across the table.
What's the difference between `get_row` and `list_rows` in Baserow MCP Server? +
Use get_row when you know the exact unique ID of a record. Use list_rows when you need to search, filter, or paginate through multiple potential records.
Do I need to know the field names before using `create_row`? +
Yes. You should use list_fields first. This shows you the exact field names and data types required before you attempt to create a new row.
How do I update a row using the `update_row` tool? +
You must provide the unique Row ID and the field name/new value pair in the request. The agent handles the JSON formatting for you.
What tools do I use to manage the structure of my database with Baserow MCP Server? +
You use list_fields and list_tables to manage the structure. These tools let your agent view the schema, helping you understand which fields exist before you try to read or write data.
How do I handle searching and retrieving multiple records using `list_rows`? +
The list_rows tool supports search and pagination. You pass search criteria and pagination limits to the tool call, letting your agent retrieve large datasets efficiently.
Can I programmatically delete a record using the `delete_row` tool? +
Yes, you pass the specific row ID to the delete_row tool. Your agent executes the deletion, removing the record from the table immediately.
How do I find my Baserow Database Token? +
Log in to your account, navigate to Settings > Database Tokens, and create a new token with appropriate workspace permissions.
Can I search for records via AI? +
Yes! The search_rows tool allows your agent to find records across a specific table matching your search criteria programmatically.
How do I find Table and Database IDs? +
Use the list_applications tool to find Database IDs, and list_database_tables to find Table IDs within a specific application.
Use it with your favorite AI tools
Connect this server to Cursor, Claude, VS Code, and more.
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