How to Use the Baserow MCP in LlamaIndex

Q: How do I connect LlamaIndex to my Baserow database?

Install llama-index-tools-mcp and instantiate a McpToolSpec. The spec fetches all the Baserow tools, like listrows, and makes them available to your LlamaIndex agent automatically.

Q: Can I use LlamaIndex to search my Baserow tables with natural language?

Yes, that's the point. Use the listrows tool to pull data from a Baserow table into a LlamaIndex vector index. You can then query that index using natural language to find the information you need.

Turn your Baserow data into a searchable knowledge base for your LlamaIndex RAG apps.

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Connect Baserow MCP to LlamaIndex

Create your Vinkius account to connect Baserow to LlamaIndex and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup Baserow with LlamaIndex

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Index Live Database Content

This isn't just about calling an API. With LlamaIndex, the output of every tool call becomes part of your knowledge base. Use `list_rows` to pull records from a Baserow table, and LlamaIndex automatically chunks and embeds that data into a vector store for you. Now your agent can perform semantic searches over your actual database content. Ask "which projects are behind schedule?" and the RAG pipeline will find the relevant rows from Baserow you just indexed. This grounds the answer in live data, not a guess.

Build Self-Aware RAG Pipelines

Your agent can query its own knowledge base about the database structure. First, run `list_tables` and `list_fields` and index their output. Now, the agent has a searchable memory of your entire Baserow schema. When you ask it to create a new entry, it can query its index to confirm the required fields and data types before calling `create_row`. This avoids errors and makes your agent much more reliable. It's using past API calls as context for future ones.

Query-Driven Data Modification with a LlamaIndex MCP Server

Combine retrieval and action in one powerful loop. A query can first use the index to find a specific record based on a vague description, like "the user who signed up last week." The retrieval engine gives you the exact `row_id`. Then, your agent can pass that `row_id` directly to the `update_row` or `delete_row` tool to modify the database. You search semantically, get a specific ID, and then take a precise action. This is what knowledge-augmented AI is built for.

Setup guide

Set up Baserow MCP in LlamaIndex

Prerequisites

Python 3.10+ installed
llama-index-tools-mcp package
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai. The MCP tools package provides BasicMCPClient and McpToolSpec.
2

Connect with BasicMCPClient
Point BasicMCPClient to your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports SSE and Streamable HTTP transports.
3

Convert to LlamaIndex tools
Call mcp_tool_spec.to_tool_list_async() to convert all Baserow MCP tools into native FunctionTool objects that any LlamaIndex agent can use.
4

Run with any LLM
Create a FunctionAgent with the tools and your preferred LLM. Swap OpenAI for Anthropic, Gemini, or any LlamaIndex-supported provider.

agent.py

from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

# Connect to the MCP
mcp_client = BasicMCPClient(
    "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)
mcp_tool_spec = McpToolSpec(client=mcp_client)

# Convert MCP tools to LlamaIndex tools
tools = await mcp_tool_spec.to_tool_list_async()

# Create and run the agent
agent = FunctionAgent(
    tools=tools,
    llm=OpenAI(model="gpt-4o"),
    system_prompt="You have access to Baserow tools.",
)
response = await agent.run("List recent Baserow data")

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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.

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Common questions about Baserow MCP in LlamaIndex

Install `llama-index-tools-mcp` and instantiate a `McpToolSpec`. The spec fetches all the Baserow tools, like `list_rows`, and makes them available to your LlamaIndex agent automatically.

Yes, that's the point. Use the `list_rows` tool to pull data from a Baserow table into a LlamaIndex vector index. You can then query that index using natural language to find the information you need.

LlamaIndex creates an index of the data you retrieve. You control what gets indexed. If you use `list_rows` on a specific table, only that data will be vectorized and stored for your RAG application.

Absolutely. After your agent retrieves information and decides on an action, it can call the `update_row` or `create_row` tools. The `McpToolSpec` makes these actions available just like any other LlamaIndex tool.

Yes. Your Vinkius token authenticates every request. The MCP server itself is stateless and only handles the specific Baserow row data or field lists passed through tools like `get_row`. Each operation runs in a zero-trust, isolated sandbox.

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