Baserow
Baserow is an open-source database tool that lets teams build no-code data applications, collaborate on records, and integrate with other services for data management
Verdict
Common use cases
- Pull customer records into support chats
- Audit project tracker tables before standups
- Check inventory levels during planning calls
- List available databases for new team members
- Surface table schemas for data migration tasks
Integration
- Vendor
- Baserow
- Category
- other
- Auth
- API_KEY
- Tools
- 2
- Composio slug
baserow
Tools
- List Databases
This tool retrieves a list of all databases in a specified workspace. as a fundamental operation, it allows users to discover which databases are available in their baserow workspace. this operation is independent and requires only authenti
- List Tables in Database
This tool lists all tables within a specified baserow database. it allows users to retrieve information about all tables in a database by using the get /api/database/{database id}/tables/ endpoint. the expected output is an array of table o
Setup
Setup guide
- 11. In Switchy, open your workspace settings and navigate to the MCP Integrations section. 2. Click 'Add Integration' and select Baserow from the list. 3. You'll need a Baserow API token — generate one by logging into your Baserow account, going to Settings > API Tokens, and creating a new token with read access to the workspaces you want to query. 4. Paste the token into Switchy's connection form and save. 5. To test, open any Space and type '@Baserow list databases in workspace [workspace_id]' — replace [workspace_id] with your actual workspace ID from Baserow. 6. If the MCP responds with a list of databases, the connection is live. 7. You can now @mention Baserow in any conversation to pull table schemas or database inventories on demand.
What teammates see: by default, memories from Baserow are scoped to the Space (PROJECT visibility) - you can mark any memory PRIVATE or share it ORG-wide.
Works well with
Top models
Compatibility data appears once enough Spaces have used this MCP together with a given model.
How Switchy teams use it
Starter prompts
List All Databases
@Baserow list all databases in workspace 1234. Show me the database names and IDs so I can decide which one to query next.Open in a Space →
Show Tables in Database
@Baserow list all tables in database 5678. Include table names and any metadata you can find about row counts or field types.Open in a Space →
Find Customer Data Table
@Baserow list databases in workspace 1234, then check each one for a table named 'Customers' or 'Contacts'. Tell me which database it's in.Open in a Space →
Audit Project Trackers
@Baserow list tables in database 9101. I need to verify we have tables for tasks, milestones, and team assignments before the meeting.Open in a Space →
Onboard New Team Member
@Baserow list all databases in workspace 1234, then for each database list the tables. Summarize what data we store and where.Open in a Space →
Example outputs
Illustrative - representative of the model's voice and quality, not literal recordings.
@baserow list all databases in workspace 42 and show me which ones have been updated in the last week
Found 5 databases in workspace 42. Three have recent activity: 'Customer Feedback' (last modified 2 days ago, 8 tables), 'Q1 Planning' (last modified 4 days ago, 3 tables), and 'Product Roadmap' (last modified 6 days ago, 12 tables). The other two databases—'Archive 2023' and 'Templates'—haven't been touched in over a month. All databases are accessible with your current API key scope.
This example shows how the MCP surfaces workspace structure and pairs raw database metadata with AI reasoning to filter by recency. Requires workspace-level read permissions. The MCP returns database IDs and timestamps; the AI interprets 'last week' and ranks results accordingly.
@baserow show me all tables in the Customer Feedback database and tell me which one likely holds survey responses
The 'Customer Feedback' database contains 4 tables: 'Survey Responses' (247 rows, fields include Email, Rating, Comments, Submitted Date), 'Follow-up Actions' (18 rows), 'NPS Tracker' (52 rows), and 'Contact List' (189 rows). Based on field names and row count, 'Survey Responses' is almost certainly where your survey data lives—it has the timestamp and free-text fields typical of form submissions.
Demonstrates the MCP's ability to introspect table schemas and the AI's ability to infer purpose from field names. The MCP fetches table metadata (field types, row counts) but doesn't read actual row data in this example. Useful for orienting yourself in an unfamiliar Baserow workspace before writing queries.
@baserow compare the structure of the Q1 Planning and Q2 Planning databases and flag any missing tables in Q2
Q1 Planning has 3 tables: 'Initiatives', 'Budget Breakdown', and 'Team Assignments'. Q2 Planning has only 2 tables: 'Initiatives' and 'Budget Breakdown'. The 'Team Assignments' table is missing from Q2. Both 'Initiatives' tables share similar field structures (Name, Owner, Status, Due Date), but Q2's version adds a 'Risk Level' field not present in Q1.
This synthesis example shows the AI reasoning over multiple List Tables calls to spot structural gaps across databases. The MCP provides schema metadata; the AI performs the diff. Helpful for maintaining consistency across planning cycles or catching setup errors. No write operations occur—this is purely analytical.
Use-case deep-dives
When Baserow beats a full CRM for small support teams
A 6-person support team handling 200 tickets a week can use Baserow as a lightweight ticket database without paying for Zendesk or Intercom. The List Databases and List Tables tools let Switchy pull ticket status, assignee, and priority fields into a shared workspace where the team triages during daily standups. The MCP requires an API key, so one admin sets it up and the whole team gets read access through Switchy. This works until ticket volume hits 1,000+ per week or you need SLA automation—at that scale, a purpose-built ticketing system with webhooks and routing rules becomes worth the cost. If your team is under 10 people and tickets live in a spreadsheet today, Baserow through Switchy is the fastest path to structured triage without vendor lock-in.
Baserow as a shared feature request database for product teams
A 4-person product team collecting feature requests from sales, support, and user interviews can centralize them in a Baserow database with fields for source, votes, and effort estimate. The List Tables tool surfaces all request categories (integrations, UI fixes, new modules) in Switchy, so the PM can query "show me high-vote, low-effort requests" during quarterly planning without opening Baserow's UI. This setup works when requests come in at 20-50 per month and the team reviews them manually. If request volume exceeds 200 per month or you need automated scoring and Slack notifications, a tool like Productboard or Canny with built-in workflows makes more sense. For early-stage teams still figuring out their prioritization framework, Baserow keeps the process flexible and the data portable.
When Baserow replaces Airtable for solo consultants
A solo consultant or 2-person agency managing 8-12 client projects at once can use Baserow as a self-hosted alternative to Airtable for intake forms, deliverable tracking, and invoice status. The List Databases tool lets Switchy pull project metadata (client name, deadline, payment status) into a workspace where the consultant reviews weekly priorities without switching tabs. Baserow's API key auth means no OAuth dance, and the 2-tool MCP is fast enough for databases under 500 rows. This breaks down if you need Zapier-style automations or public client portals—Baserow's automation layer is lighter than Airtable's. If you're already paying for Airtable and using advanced formulas or synced bases, migration isn't worth it. But if you want to own your data and avoid per-seat pricing, Baserow through Switchy is the cleanest swap.
Frequently asked
What does the Baserow MCP do in Switchy?
It lets your AI agents list databases and tables in your Baserow workspace. You can query which databases exist, then drill into any database to see its table structure. This is useful for discovery and building workflows that reference specific Baserow tables, but the MCP doesn't read or write row data yet.
Do I need admin access to connect Baserow?
You need a Baserow API key with read permissions on the workspace you want to query. Baserow generates these keys in your account settings. The key must have access to the databases you want the MCP to list — workspace-level read access is typically sufficient.
Can the Baserow MCP create or update rows?
No. This MCP only lists databases and tables. It doesn't read row data, create records, update fields, or delete anything. If you need to manipulate Baserow data, you'll use Baserow's API directly or wait for an expanded MCP that exposes those operations.
Why use this instead of just calling the Baserow API?
The MCP gives your AI agents a structured way to discover what's in Baserow without you writing API wrappers. If your workflow is just "show me what databases exist", the MCP is faster than building a custom integration. For anything beyond listing, you still need the API.
Who on the team should connect the Baserow MCP?
Whoever owns the Baserow workspace or has permission to generate API keys. Once connected in Switchy, any team member with access to the shared workspace can use the MCP in their prompts. The connection itself doesn't count against Baserow seat limits.