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.

Baserow MCP Server for CrewAI 10 tools — connect in under 2 minutes

Name: Baserow
Availability: InStock
Author: Vinkius

docs.crewai.com/mcp/overview

Built by Vinkius GDPR 10 Tools Framework

Connect your CrewAI agents to Baserow through Vinkius, pass the Edge URL in the `mcps` parameter and every Baserow tool is auto-discovered at runtime. No credentials to manage, no infrastructure to maintain.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

python

from crewai import Agent, Task, Crew

agent = Agent(
    role="Baserow Specialist",
    goal="Help users interact with Baserow effectively",
    backstory=(
        "You are an expert at leveraging Baserow tools "
        "for automation and data analysis."
    ),
    # Your Vinkius token. get it at cloud.vinkius.com
    mcps=["https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"],
)

task = Task(
    description=(
        "Explore all available tools in Baserow "
        "and summarize their capabilities."
    ),
    agent=agent,
    expected_output=(
        "A detailed summary of 10 available tools "
        "and what they can do."
    ),
)

crew = Crew(agents=[agent], tasks=[task])
result = crew.kickoff()
print(result)

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Stream every event to Splunk, Datadog, or your own webhook in real-time

* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure

About Baserow MCP Server

Connect your Baserow databases to any AI agent and take full control of your data through natural conversation.

When paired with CrewAI, Baserow becomes a first-class tool in your multi-agent workflows. Each agent in the crew can call Baserow tools autonomously, one agent queries data, another analyzes results, a third compiles reports, all orchestrated through Vinkius with zero configuration overhead.

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

The Baserow MCP Server exposes 10 tools through the Vinkius. Connect it to CrewAI in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

How to Connect Baserow to CrewAI via MCP

Follow these steps to integrate the Baserow MCP Server with CrewAI.

Install CrewAI

Run pip install crewai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token from cloud.vinkius.com

Customize the agent

Adjust the role, goal, and backstory to fit your use case

Run the crew

Run python crew.py. CrewAI auto-discovers 10 tools from Baserow

Why Use CrewAI with the Baserow MCP Server

CrewAI Multi-Agent Orchestration Framework provides unique advantages when paired with Baserow through the Model Context Protocol.

Multi-agent collaboration lets you decompose complex workflows into specialized roles, one agent researches, another analyzes, a third generates reports, each with access to MCP tools

CrewAI's native MCP integration requires zero adapter code: pass Vinkius Edge URL directly in the `mcps` parameter and agents auto-discover every available tool at runtime

Built-in task delegation and shared memory mean agents can pass context between steps without manual state management, enabling multi-hop reasoning across tool calls

Sequential and hierarchical crew patterns map naturally to real-world workflows: enumerate subdomains → analyze DNS history → check WHOIS records → compile findings into actionable reports

Baserow + CrewAI Use Cases

Practical scenarios where CrewAI combined with the Baserow MCP Server delivers measurable value.

Automated multi-step research: a reconnaissance agent queries Baserow for raw data, then a second analyst agent cross-references findings and flags anomalies. all without human handoff

Scheduled intelligence reports: set up a crew that periodically queries Baserow, analyzes trends over time, and generates executive briefings in markdown or PDF format

Multi-source enrichment pipelines: chain Baserow tools with other MCP servers in the same crew, letting agents correlate data across multiple providers in a single workflow

Compliance and audit automation: a compliance agent queries Baserow against predefined policy rules, generates deviation reports, and routes findings to the appropriate team

Baserow MCP Tools for CrewAI (10)

These 10 tools become available when you connect Baserow to CrewAI via MCP:

create_row

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

delete_row

Provide the table ID and row ID. WARNING: this action is irreversible. Delete a row from a Baserow table

get_row

Field names are returned in user-readable format. Provide the table ID and row ID. Get a specific row from a Baserow table

get_table

Provide the table ID from list_tables. Get details for a specific Baserow table

list_databases

Each database shows its ID, name, workspace and creation date. Use this to discover available databases before querying their tables. List all Baserow databases

list_fields

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

list_rows

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

list_tables

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

list_views

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

update_row

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

Example Prompts for Baserow in CrewAI

Ready-to-use prompts you can give your CrewAI agent to start working with Baserow immediately.

"List all tables in my Baserow workspace."

"Show me all rows in the Tasks table where Status is 'In Progress'."

"Create a new task called 'Review PR #234' assigned to Alice with status 'To Do'."

Troubleshooting Baserow MCP Server with CrewAI

Common issues when connecting Baserow to CrewAI through the Vinkius, and how to resolve them.

MCP tools not discovered

Ensure the Edge URL is correct. CrewAI connects lazily when the crew starts. check console output.

Agent not using tools

Make the task description specific. Instead of "do something", say "Use the available tools to list contacts".

Timeout errors

CrewAI has a 10s connection timeout by default. Ensure your network can reach the Edge URL.

Rate limiting or 429 errors

Vinkius enforces per-token rate limits. Check your subscription tier and request quota in the dashboard. Upgrade if you need higher throughput.

Baserow + CrewAI FAQ

Common questions about integrating Baserow MCP Server with CrewAI.

How does CrewAI discover and connect to MCP tools?

CrewAI connects to MCP servers lazily. when the crew starts, each agent resolves its MCP URLs and fetches the tool catalog via the standard tools/list method. This means tools are always fresh and reflect the server's current capabilities. No tool schemas need to be hardcoded.

Can different agents in the same crew use different MCP servers?

Yes. Each agent has its own mcps list, so you can assign specific servers to specific roles. For example, a reconnaissance agent might use a domain intelligence server while an analysis agent uses a vulnerability database server.

What happens when an MCP tool call fails during a crew run?

CrewAI wraps tool failures as context for the agent. The LLM receives the error message and can decide to retry with different parameters, fall back to a different tool, or mark the task as partially complete. This resilience is critical for production workflows.

Can CrewAI agents call multiple MCP tools in parallel?

CrewAI agents execute tool calls sequentially within a single reasoning step. However, you can run multiple agents in parallel using process=Process.parallel, each calling different MCP tools concurrently. This is ideal for workflows where separate data sources need to be queried simultaneously.

Can I run CrewAI crews on a schedule (cron)?

Yes. CrewAI crews are standard Python scripts, so you can invoke them via cron, Airflow, Celery, or any task scheduler. The crew.kickoff() method runs synchronously by default, making it straightforward to integrate into existing pipelines.