# How to integrate Baserow MCP with CrewAI

```json
{
  "title": "How to integrate Baserow MCP with CrewAI",
  "toolkit": "Baserow",
  "toolkit_slug": "baserow",
  "framework": "CrewAI",
  "framework_slug": "crew-ai",
  "url": "https://composio.dev/toolkits/baserow/framework/crew-ai",
  "markdown_url": "https://composio.dev/toolkits/baserow/framework/crew-ai.md",
  "updated_at": "2026-05-12T10:02:31.548Z"
}
```

## Introduction

This guide walks you through connecting Baserow to CrewAI using the Composio tool router. By the end, you'll have a working Baserow agent that can list all databases in your main workspace, show tables in the marketing database, get details for tables in project database through natural language commands.
This guide will help you understand how to give your CrewAI agent real control over a Baserow account through Composio's Baserow MCP server.
Before we dive in, let's take a quick look at the key ideas and tools involved.

## Also integrate Baserow with

- [OpenAI Agents SDK](https://composio.dev/toolkits/baserow/framework/open-ai-agents-sdk)
- [Claude Agent SDK](https://composio.dev/toolkits/baserow/framework/claude-agents-sdk)
- [Claude Code](https://composio.dev/toolkits/baserow/framework/claude-code)
- [Claude Cowork](https://composio.dev/toolkits/baserow/framework/claude-cowork)
- [Codex](https://composio.dev/toolkits/baserow/framework/codex)
- [OpenClaw](https://composio.dev/toolkits/baserow/framework/openclaw)
- [Hermes](https://composio.dev/toolkits/baserow/framework/hermes-agent)
- [CLI](https://composio.dev/toolkits/baserow/framework/cli)
- [Google ADK](https://composio.dev/toolkits/baserow/framework/google-adk)
- [LangChain](https://composio.dev/toolkits/baserow/framework/langchain)
- [Vercel AI SDK](https://composio.dev/toolkits/baserow/framework/ai-sdk)
- [Mastra AI](https://composio.dev/toolkits/baserow/framework/mastra-ai)
- [LlamaIndex](https://composio.dev/toolkits/baserow/framework/llama-index)

## TL;DR

Here's what you'll learn:
- Get a Composio API key and configure your Baserow connection
- Set up CrewAI with an MCP enabled agent
- Create a Tool Router session or standalone MCP server for Baserow
- Build a conversational loop where your agent can execute Baserow operations

## What is CrewAI?

CrewAI is a powerful framework for building multi-agent AI systems. It provides primitives for defining agents with specific roles, creating tasks, and orchestrating workflows through crews.
Key features include:
- Agent Roles: Define specialized agents with specific goals and backstories
- Task Management: Create tasks with clear descriptions and expected outputs
- Crew Orchestration: Combine agents and tasks into collaborative workflows
- MCP Integration: Connect to external tools through Model Context Protocol

## What is the Baserow MCP server, and what's possible with it?

The Baserow MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Baserow account. It provides structured and secure access to your Baserow workspaces and databases, so your agent can perform actions like discovering databases, listing tables, and streamlining workspace exploration on your behalf.
- Workspace database discovery: Have your agent quickly list all databases within any of your Baserow workspaces, making it easy to navigate large projects.
- Table enumeration in databases: Let your agent fetch a full list of tables for any selected database, helping you understand and manage your data structures.
- Metadata retrieval for planning: Enable your agent to gather essential metadata about databases and tables, laying the groundwork for more advanced automations or integrations.
- Seamless data navigation: Guide your agent to explore and map your Baserow environment, so it can support you in building custom workflows or data pipelines.

## Supported Tools

| Tool slug | Name | Description |
|---|---|---|
| `BASEROW_CREATE_USER` | Create User | Tool to create a new Baserow user with the provided details. Use when you need to register a new user account in Baserow. After creating an account, an initial workspace containing a database is automatically created. Optionally generates authentication JWT tokens if authenticate parameter is set to true. |
| `BASEROW_DISPATCH_BUILDER_PAGE_DATA_SOURCE` | Dispatch Builder Page Data Source | Tool to dispatch the service of a builder page data source and return the result. Use when you need to execute a data source query in Baserow's builder application. |
| `BASEROW_DISPATCH_PUBLIC_BUILDER_PAGE_DATA_SOURCE` | Dispatch Public Builder Page Data Source | Tool to dispatch the service of a published builder page data source and return the result. Use this when you need to execute a data source in a public/published Baserow builder domain. |
| `BASEROW_GET_FORM_VIEW_METADATA` | Get Form View Metadata | Tool to retrieve metadata for a Baserow form view. Use when you need to get form structure and configuration details for constructing a form with the right fields. The form must be publicly shared or the user must have access to the related workspace. |
| `BASEROW_GET_PUBLIC_BUILDER_BY_DOMAIN_NAME` | Get Public Builder by Domain Name | Tool to retrieve the public published version of a builder by its domain name. Use when you need to access a published Baserow builder application and its configuration, including pages, scripts, theme, and user sources. |
| `BASEROW_GET_RECORD_NAMES_BUILDER_PAGE_DATA_SOURCE` | Get Record Names for Builder Page Data Source | Tool to find the record names associated with a given list of record ids. Use when you need to retrieve the display names for specific records from a builder page data source. |
| `BASEROW_GET_SETTINGS` | Get Settings | Tool to retrieve all admin configured settings for the Baserow instance. Use when you need to check system-wide configuration like signup policies, email verification settings, or workspace creation permissions. |
| `BASEROW_LIST_APPLICATION_USER_SOURCES` | List Application User Sources | Tool to list all user sources of an application if the user has access to the related application's workspace. Use when you need to retrieve user source configurations for a Baserow application. If the workspace is related to a template, this endpoint is publicly accessible. |
| `BASEROW_LIST_AUTH_PROVIDERS_LOGIN_OPTIONS` | List Auth Providers Login Options | Tool to list available login options for configured authentication providers. Use when you need to discover which authentication methods are enabled for the Baserow instance. |
| `BASEROW_LIST_DATABASES` | 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 authentication in order to fetch essential metadata for subsequent operations. |
| `BASEROW_LIST_TABLES` | 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 objects containing details such as id, name, order, database_id, type, and first_row_header. |
| `BASEROW_LIST_TEMPLATES` | List Templates | Tool to list all template categories and their related templates. The template's workspace_id can be used for previewing purposes because that workspace contains publicly accessible applications. Use when you need to discover available templates in Baserow. |
| `BASEROW_SEND_PASSWORD_RESET_EMAIL` | Send Password Reset Email | Tool to send a password reset email to a user's email address. The email contains a password reset link that is valid for 48 hours. The endpoint will not fail if the email address is not found. |
| `BASEROW_SEND_VERIFY_EMAIL` | Send Verification Email | Tool to send a verification email to a user's email address. Use when you need to trigger email verification for a user account that hasn't been verified yet. |

## Supported Triggers

None listed.

## Creating MCP Server - Stand-alone vs Composio SDK

The Baserow MCP server is an implementation of the Model Context Protocol that connects your AI agent to Baserow. It provides structured and secure access so your agent can perform Baserow operations on your behalf through a secure, permission-based interface.
With Composio's managed implementation, you don't have to create your own developer app. For production, if you're building an end product, we recommend using your own credentials. The managed server helps you prototype fast and go from 0-1 faster.

## Step-by-step Guide

### 1. Prerequisites

Before starting, make sure you have:
- Python 3.9 or higher
- A Composio account and API key
- A Baserow connection authorized in Composio
- An OpenAI API key for the CrewAI LLM
- Basic familiarity with Python

### 1. Getting API Keys for OpenAI and Composio

OpenAI API Key
- Go to the [OpenAI dashboard](https://platform.openai.com/settings/organization/api-keys) and create an API key. You'll need credits to use the models, or you can connect to another model provider.
- Keep the API key safe.
Composio API Key
- Log in to the [Composio dashboard](https://dashboard.composio.dev?utm_source=toolkits&utm_medium=framework_docs).
- Navigate to your API settings and generate a new API key.
- Store this key securely as you'll need it for authentication.

### 2. Install dependencies

**What's happening:**
- composio connects your agent to Baserow via MCP
- crewai provides Agent, Task, Crew, and LLM primitives
- crewai-tools[mcp] includes MCP helpers
- python-dotenv loads environment variables from .env
```bash
pip install composio crewai crewai-tools[mcp] python-dotenv
```

### 3. Set up environment variables

Create a .env file in your project root.
What's happening:
- COMPOSIO_API_KEY authenticates with Composio
- USER_ID scopes the session to your account
- OPENAI_API_KEY lets CrewAI use your chosen OpenAI model
```bash
COMPOSIO_API_KEY=your_composio_api_key_here
USER_ID=your_user_id_here
OPENAI_API_KEY=your_openai_api_key_here
```

### 4. Import dependencies

**What's happening:**
- CrewAI classes define agents and tasks, and run the workflow
- MCPServerHTTP connects the agent to an MCP endpoint
- Composio will give you a short lived Baserow MCP URL
```python
import os
from composio import Composio
from crewai import Agent, Task, Crew
from crewai_tools import MCPServerAdapter
import dotenv

dotenv.load_dotenv()

COMPOSIO_API_KEY = os.getenv("COMPOSIO_API_KEY")
COMPOSIO_USER_ID = os.getenv("COMPOSIO_USER_ID")

if not COMPOSIO_API_KEY:
    raise ValueError("COMPOSIO_API_KEY is not set")
if not COMPOSIO_USER_ID:
    raise ValueError("COMPOSIO_USER_ID is not set")
```

### 5. Create a Composio Tool Router session for Baserow

**What's happening:**
- You create a Baserow only session through Composio
- Composio returns an MCP HTTP URL that exposes Baserow tools
```python
composio_client = Composio(api_key=COMPOSIO_API_KEY)
session = composio_client.create(user_id=COMPOSIO_USER_ID, toolkits=["baserow"])

url = session.mcp.url
```

### 6. Initialize the MCP Server

**What's Happening:**
- Server Configuration: The code sets up connection parameters including the MCP server URL, streamable HTTP transport, and Composio API key authentication.
- MCP Adapter Bridge: MCPServerAdapter acts as a context manager that converts Composio MCP tools into a CrewAI-compatible format.
- Agent Setup: Creates a CrewAI Agent with a defined role (Search Assistant), goal (help with internet searches), and access to the MCP tools.
- Configuration Options: The agent includes settings like verbose=False for clean output and max_iter=10 to prevent infinite loops.
- Dynamic Tool Usage: Once created, the agent automatically accesses all Composio Search tools and decides when to use them based on user queries.
```python
server_params = {
    "url": url,
    "transport": "streamable-http",
    "headers": {"x-api-key": COMPOSIO_API_KEY},
}

with MCPServerAdapter(server_params) as tools:
    agent = Agent(
        role="Search Assistant",
        goal="Help users search the internet effectively",
        backstory="You are a helpful assistant with access to search tools.",
        tools=tools,
        verbose=False,
        max_iter=10,
    )
```

### 7. Create a CLI Chatloop and define the Crew

**What's Happening:**
- Interactive CLI Setup: The code creates an infinite loop that continuously prompts for user input and maintains the entire conversation history in a string variable.
- Input Validation: Empty inputs are ignored to prevent processing blank messages and keep the conversation clean.
- Context Building: Each user message is appended to the conversation context, which preserves the full dialogue history for better agent responses.
- Dynamic Task Creation: For every user input, a new Task is created that includes both the full conversation history and the current request as context.
- Crew Execution: A Crew is instantiated with the agent and task, then kicked off to process the request and generate a response.
- Response Management: The agent's response is converted to a string, added to the conversation context, and displayed to the user, maintaining conversational continuity.
```python
print("Chat started! Type 'exit' or 'quit' to end.\n")

conversation_context = ""

while True:
    user_input = input("You: ").strip()

    if user_input.lower() in ["exit", "quit", "bye"]:
        print("\nGoodbye!")
        break

    if not user_input:
        continue

    conversation_context += f"\nUser: {user_input}\n"
    print("\nAgent is thinking...\n")

    task = Task(
        description=(
            f"Conversation history:\n{conversation_context}\n\n"
            f"Current request: {user_input}"
        ),
        expected_output="A helpful response addressing the user's request",
        agent=agent,
    )

    crew = Crew(agents=[agent], tasks=[task], verbose=False)
    result = crew.kickoff()
    response = str(result)

    conversation_context += f"Agent: {response}\n"
    print(f"Agent: {response}\n")
```

## Complete Code

```python
from crewai import Agent, Task, Crew, LLM
from crewai_tools import MCPServerAdapter
from composio import Composio
from dotenv import load_dotenv
import os

load_dotenv()

GOOGLE_API_KEY = os.getenv("GOOGLE_API_KEY")
COMPOSIO_API_KEY = os.getenv("COMPOSIO_API_KEY")
COMPOSIO_USER_ID = os.getenv("COMPOSIO_USER_ID")

if not GOOGLE_API_KEY:
    raise ValueError("GOOGLE_API_KEY is not set in the environment.")
if not COMPOSIO_API_KEY:
    raise ValueError("COMPOSIO_API_KEY is not set in the environment.")
if not COMPOSIO_USER_ID:
    raise ValueError("COMPOSIO_USER_ID is not set in the environment.")

# Initialize Composio and create a session
composio = Composio(api_key=COMPOSIO_API_KEY)
session = composio.create(
    user_id=COMPOSIO_USER_ID,
    toolkits=["baserow"],
)
url = session.mcp.url

# Configure LLM
llm = LLM(
    model="gpt-5",
    api_key=os.getenv("OPENAI_API_KEY"),
)

server_params = {
    "url": url,
    "transport": "streamable-http",
    "headers": {"x-api-key": COMPOSIO_API_KEY},
}

with MCPServerAdapter(server_params) as tools:
    agent = Agent(
        role="Search Assistant",
        goal="Help users with internet searches",
        backstory="You are an expert assistant with access to Composio Search tools.",
        tools=tools,
        llm=llm,
        verbose=False,
        max_iter=10,
    )

    print("Chat started! Type 'exit' or 'quit' to end.\n")

    conversation_context = ""

    while True:
        user_input = input("You: ").strip()

        if user_input.lower() in ["exit", "quit", "bye"]:
            print("\nGoodbye!")
            break

        if not user_input:
            continue

        conversation_context += f"\nUser: {user_input}\n"
        print("\nAgent is thinking...\n")

        task = Task(
            description=(
                f"Conversation history:\n{conversation_context}\n\n"
                f"Current request: {user_input}"
            ),
            expected_output="A helpful response addressing the user's request",
            agent=agent,
        )

        crew = Crew(agents=[agent], tasks=[task], verbose=False)
        result = crew.kickoff()
        response = str(result)

        conversation_context += f"Agent: {response}\n"
        print(f"Agent: {response}\n")
```

## Conclusion

You now have a CrewAI agent connected to Baserow through Composio's Tool Router. The agent can perform Baserow operations through natural language commands.
Next steps:
- Add role-specific instructions to customize agent behavior
- Plug in more toolkits for multi-app workflows
- Chain tasks for complex multi-step operations

## How to build Baserow MCP Agent with another framework

- [OpenAI Agents SDK](https://composio.dev/toolkits/baserow/framework/open-ai-agents-sdk)
- [Claude Agent SDK](https://composio.dev/toolkits/baserow/framework/claude-agents-sdk)
- [Claude Code](https://composio.dev/toolkits/baserow/framework/claude-code)
- [Claude Cowork](https://composio.dev/toolkits/baserow/framework/claude-cowork)
- [Codex](https://composio.dev/toolkits/baserow/framework/codex)
- [OpenClaw](https://composio.dev/toolkits/baserow/framework/openclaw)
- [Hermes](https://composio.dev/toolkits/baserow/framework/hermes-agent)
- [CLI](https://composio.dev/toolkits/baserow/framework/cli)
- [Google ADK](https://composio.dev/toolkits/baserow/framework/google-adk)
- [LangChain](https://composio.dev/toolkits/baserow/framework/langchain)
- [Vercel AI SDK](https://composio.dev/toolkits/baserow/framework/ai-sdk)
- [Mastra AI](https://composio.dev/toolkits/baserow/framework/mastra-ai)
- [LlamaIndex](https://composio.dev/toolkits/baserow/framework/llama-index)

## Related Toolkits

- [Supabase](https://composio.dev/toolkits/supabase) - Supabase is an open-source backend platform offering scalable Postgres databases, authentication, storage, and real-time APIs. It lets developers build modern apps without managing infrastructure.
- [Codeinterpreter](https://composio.dev/toolkits/codeinterpreter) - Codeinterpreter is a Python-based coding environment with built-in data analysis and visualization. It lets you instantly run scripts, plot results, and prototype solutions inside supported platforms.
- [GitHub](https://composio.dev/toolkits/github) - GitHub is a code hosting platform for version control and collaborative software development. It streamlines project management, code review, and team workflows in one place.
- [Ably](https://composio.dev/toolkits/ably) - Ably is a real-time messaging platform for live chat and data sync in modern apps. It offers global scale and rock-solid reliability for seamless, instant experiences.
- [Abuselpdb](https://composio.dev/toolkits/abuselpdb) - Abuselpdb is a central database for reporting and checking IPs linked to malicious online activity. Use it to quickly identify and report suspicious or abusive IP addresses.
- [Alchemy](https://composio.dev/toolkits/alchemy) - Alchemy is a blockchain development platform offering APIs and tools for Ethereum apps. It simplifies building and scaling Web3 projects with robust infrastructure.
- [Algolia](https://composio.dev/toolkits/algolia) - Algolia is a hosted search API that powers lightning-fast, relevant search experiences for web and mobile apps. It helps developers deliver instant, typo-tolerant, and scalable search without complex infrastructure.
- [Anchor browser](https://composio.dev/toolkits/anchor_browser) - Anchor browser is a developer platform for AI-powered web automation. It transforms complex browser actions into easy API endpoints for streamlined web interaction.
- [Apiflash](https://composio.dev/toolkits/apiflash) - Apiflash is a website screenshot API for programmatically capturing web pages. It delivers high-quality screenshots on demand for automation, monitoring, or reporting.
- [Apiverve](https://composio.dev/toolkits/apiverve) - Apiverve delivers a suite of powerful APIs that simplify integration for developers. It's designed for reliability and scalability so you can build faster, smarter applications without the integration headache.
- [Appcircle](https://composio.dev/toolkits/appcircle) - Appcircle is an enterprise-grade mobile CI/CD platform for building, testing, and publishing mobile apps. It streamlines mobile DevOps so teams ship faster and with more confidence.
- [Appdrag](https://composio.dev/toolkits/appdrag) - Appdrag is a cloud platform for building websites, APIs, and databases with drag-and-drop tools and code editing. It accelerates development and iteration by combining hosting, database management, and low-code features in one place.
- [Appveyor](https://composio.dev/toolkits/appveyor) - AppVeyor is a cloud-based continuous integration service for building, testing, and deploying applications. It helps developers automate and streamline their software delivery pipelines.
- [Backendless](https://composio.dev/toolkits/backendless) - Backendless is a backend-as-a-service platform for mobile and web apps, offering database, file storage, user authentication, and APIs. It helps developers ship scalable applications faster without managing server infrastructure.
- [Bench](https://composio.dev/toolkits/bench) - Bench is a benchmarking tool for automated performance measurement and analysis. It helps you quickly evaluate, compare, and track your systems or workflows.
- [Better stack](https://composio.dev/toolkits/better_stack) - Better Stack is a monitoring, logging, and incident management solution for apps and services. It helps teams ensure application reliability and performance with real-time insights.
- [Bitbucket](https://composio.dev/toolkits/bitbucket) - Bitbucket is a Git-based code hosting and collaboration platform for teams. It enables secure repository management and streamlined code reviews.
- [Blazemeter](https://composio.dev/toolkits/blazemeter) - Blazemeter is a continuous testing platform for web and mobile app performance. It empowers teams to automate and analyze large-scale tests with ease.
- [Blocknative](https://composio.dev/toolkits/blocknative) - Blocknative delivers real-time mempool monitoring and transaction management for public blockchains. Instantly track pending transactions and optimize blockchain interactions with live data.
- [Bolt iot](https://composio.dev/toolkits/bolt_iot) - Bolt IoT is a platform for building and managing IoT projects with cloud-based device control and monitoring. It makes connecting sensors and actuators to the internet seamless for automation and data insights.

## Frequently Asked Questions

### What are the differences in Tool Router MCP and Baserow MCP?

With a standalone Baserow MCP server, the agents and LLMs can only access a fixed set of Baserow tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Baserow and many other apps based on the task at hand, all through a single MCP endpoint.

### Can I use Tool Router MCP with CrewAI?

Yes, you can. CrewAI fully supports MCP integration. You get structured tool calling, message history handling, and model orchestration while Tool Router takes care of discovering and serving the right Baserow tools.

### Can I manage the permissions and scopes for Baserow while using Tool Router?

Yes, absolutely. You can configure which Baserow scopes and actions are allowed when connecting your account to Composio. You can also bring your own OAuth credentials or API configuration so you keep full control over what the agent can do.

### How safe is my data with Composio Tool Router?

All sensitive data such as tokens, keys, and configuration is fully encrypted at rest and in transit. Composio is SOC 2 Type 2 compliant and follows strict security practices so your Baserow data and credentials are handled as safely as possible.

---
[See all toolkits](https://composio.dev/toolkits) · [Composio docs](https://docs.composio.dev/llms.txt)
