Baserow MCP Server for LlamaIndex 10 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Baserow as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
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Vinkius supports streamable HTTP and SSE.
import asyncio
from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI
async def main():
# Your Vinkius token. get it at cloud.vinkius.com
mcp_client = BasicMCPClient("https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
mcp_tool_spec = McpToolSpec(client=mcp_client)
tools = await mcp_tool_spec.to_tool_list_async()
agent = FunctionAgent(
tools=tools,
llm=OpenAI(model="gpt-4o"),
system_prompt=(
"You are an assistant with access to Baserow. "
"You have 10 tools available."
),
)
response = await agent.run(
"What tools are available in Baserow?"
)
print(response)
asyncio.run(main())
* 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.
LlamaIndex agents combine Baserow tool responses with indexed documents for comprehensive, grounded answers. Connect 10 tools through Vinkius and query live data alongside vector stores and SQL databases in a single turn. ideal for hybrid search, data enrichment, and analytical workflows.
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 LlamaIndex 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 LlamaIndex via MCP
Follow these steps to integrate the Baserow MCP Server with LlamaIndex.
Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token
Run the agent
Save to agent.py and run: python agent.py
Explore tools
The agent discovers 10 tools from Baserow
Why Use LlamaIndex with the Baserow MCP Server
LlamaIndex provides unique advantages when paired with Baserow through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Baserow tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Baserow tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Baserow, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Baserow tools were called, what data was returned, and how it influenced the final answer
Baserow + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Baserow MCP Server delivers measurable value.
Hybrid search: combine Baserow real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Baserow to augment indexed data with live information before generating user-facing responses
Knowledge base agents: build agents that maintain and update knowledge bases by periodically querying Baserow for fresh data
Analytical workflows: chain Baserow queries with LlamaIndex's data connectors to build multi-source analytical reports
Baserow MCP Tools for LlamaIndex (10)
These 10 tools become available when you connect Baserow to LlamaIndex 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 LlamaIndex
Ready-to-use prompts you can give your LlamaIndex 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 LlamaIndex
Common issues when connecting Baserow to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpBaserow + LlamaIndex FAQ
Common questions about integrating Baserow MCP Server with LlamaIndex.
How does LlamaIndex connect to MCP servers?
Can I combine MCP tools with vector stores?
Does LlamaIndex support async MCP calls?
Connect Baserow with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
Anthropic's native desktop app for Claude with built-in MCP support.
AI-first code editor with integrated LLM-powered coding assistance.
GitHub Copilot in VS Code with Agent mode and MCP support.
Purpose-built IDE for agentic AI coding workflows.
Autonomous AI coding agent that runs inside VS Code.
Anthropic's agentic CLI for terminal-first development.
Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
Type-safe agent development for Python with first-class MCP support.
TypeScript toolkit for building AI-powered web applications.
TypeScript-native agent framework for modern web stacks.
Python framework for orchestrating collaborative AI agent crews.
Leading Python framework for composable LLM applications.
Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
Connect Baserow to LlamaIndex
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.