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 LangChain 10 tools — connect in under 2 minutes

Name: Baserow
Availability: InStock
Author: Vinkius

python.langchain.com/docs/integrations/tools/mcp

Built by Vinkius GDPR 10 Tools Framework

LangChain is the leading Python framework for composable LLM applications. Connect Baserow through Vinkius and LangChain agents can call every tool natively. combine them with retrievers, memory, and output parsers for sophisticated AI pipelines.

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

import asyncio
from langchain_mcp_adapters.client import MultiServerMCPClient
from langchain_openai import ChatOpenAI
from langgraph.prebuilt import create_react_agent

async def main():
    # Your Vinkius token. get it at cloud.vinkius.com
    async with MultiServerMCPClient({
        "baserow": {
            "transport": "streamable_http",
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
        }
    }) as client:
        tools = client.get_tools()
        agent = create_react_agent(
            ChatOpenAI(model="gpt-4o"),
            tools,
        )
        response = await agent.ainvoke({
            "messages": [{
                "role": "user",
                "content": "Using Baserow, show me what tools are available.",
            }]
        })
        print(response["messages"][-1].content)

asyncio.run(main())

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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.

LangChain's ecosystem of 500+ components combines seamlessly with Baserow through native MCP adapters. Connect 10 tools via Vinkius and use ReAct agents, Plan-and-Execute strategies, or custom agent architectures. with LangSmith tracing giving full visibility into every tool call, latency, and token cost.

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 LangChain 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 LangChain via MCP

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

Install dependencies

Run pip install langchain langchain-mcp-adapters langgraph langchain-openai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Run the agent

Save the code and run python agent.py

Explore tools

The agent discovers 10 tools from Baserow via MCP

Why Use LangChain with the Baserow MCP Server

LangChain provides unique advantages when paired with Baserow through the Model Context Protocol.

The largest ecosystem of integrations, chains, and agents. combine Baserow MCP tools with 500+ LangChain components

Agent architecture supports ReAct, Plan-and-Execute, and custom strategies with full MCP tool access at every step

LangSmith tracing gives you complete visibility into tool calls, latencies, and token usage for production debugging

Memory and conversation persistence let agents maintain context across Baserow queries for multi-turn workflows

Baserow + LangChain Use Cases

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

RAG with live data: combine Baserow tool results with vector store retrievals for answers grounded in both real-time and historical data

Autonomous research agents: LangChain agents query Baserow, synthesize findings, and generate comprehensive research reports

Multi-tool orchestration: chain Baserow tools with web scrapers, databases, and calculators in a single agent run

Production monitoring: use LangSmith to trace every Baserow tool call, measure latency, and optimize your agent's performance

Baserow MCP Tools for LangChain (10)

These 10 tools become available when you connect Baserow to LangChain 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 LangChain

Ready-to-use prompts you can give your LangChain 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 LangChain

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

MultiServerMCPClient not found

Install: pip install langchain-mcp-adapters

Baserow + LangChain FAQ

Common questions about integrating Baserow MCP Server with LangChain.

How does LangChain connect to MCP servers?

Use langchain-mcp-adapters to create an MCP client. LangChain discovers all tools and wraps them as native LangChain tools compatible with any agent type.

Which LangChain agent types work with MCP?

All agent types including ReAct, OpenAI Functions, and custom agents work with MCP tools. The tools appear as standard LangChain tools after the adapter wraps them.