Supabase 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 Supabase as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
ASK AI ABOUT THIS MCP SERVER
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 Supabase. "
"You have 10 tools available."
),
)
response = await agent.run(
"What tools are available in Supabase?"
)
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 Supabase MCP Server
Integrate the comprehensive backend infrastructure of Supabase straight into your conversational LLM workflows. By securely authenticating with your service_role key, your AI assistant bypasses row-level security constraints, operating as a fully-privileged database administrator. Query rows, invoke complex PL/pgSQL functions via RPC, evaluate the authenticated user roster, and audit your active storage buckets all through simple natural language commands, accelerating debugging and environment iterations without leaving the terminal.
LlamaIndex agents combine Supabase 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 Interactions — Actively query datasets using
db_select, seamlessly add new rows executingdb_insert, and modify existing data structures applyingdb_updateordb_delete. - Custom Functional Logic — Invoke pre-compiled database procedures and PL/pgSQL functions securely utilizing
db_rpcwith dynamic JSON arguments. - Authentication Tracking — Audit your userbase and confirm authentication statuses instantly fetching native rosters through
list_auth_users. - Storage Diagnostics — Inspect configured object storage containers mapping file architectures securely invoking
list_storage_buckets.
The Supabase 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 Supabase to LlamaIndex via MCP
Follow these steps to integrate the Supabase 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 Supabase
Why Use LlamaIndex with the Supabase MCP Server
LlamaIndex provides unique advantages when paired with Supabase through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Supabase tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Supabase tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Supabase, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Supabase tools were called, what data was returned, and how it influenced the final answer
Supabase + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Supabase MCP Server delivers measurable value.
Hybrid search: combine Supabase real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Supabase 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 Supabase for fresh data
Analytical workflows: chain Supabase queries with LlamaIndex's data connectors to build multi-source analytical reports
Supabase MCP Tools for LlamaIndex (10)
These 10 tools become available when you connect Supabase to LlamaIndex via MCP:
db_count
Useful for pagination and analytics. Count rows in a database table with optional filters
db_delete
A match_query is mandatory. This action is irreversible. Delete rows from a database table
db_insert
Provide the payload as a JSON string. Insert a new row into a database table
db_rpc
Provide the function name and optional JSON arguments. Execute a Supabase Postgres Function (RPC)
db_select
For filters, use match_query (e.g. "id=eq.1"). Defaults to 50 rows. Query records from any PostgreSQL database table using PostgREST syntax
db_update
A match_query is required to target specific rows (e.g. "id=eq.123"). Update existing rows in a database table
get_auth_user
Get detailed information about a specific authenticated user
list_auth_users
List authenticated users from Supabase Auth
list_storage_buckets
List all available storage buckets
list_storage_files
List files inside a storage bucket
Example Prompts for Supabase in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Supabase immediately.
"Verify the 'inventory_products' table sequentially effectively correctly querying all products labeled 'out-of-stock'."
"Trigger the custom stored procedure 'restock_items' using `db_rpc` to replenish the inventory of IDs 12 and 15 natively."
"Check all registered accounts dynamically applying `list_auth_users` for recent logins natively securely."
Troubleshooting Supabase MCP Server with LlamaIndex
Common issues when connecting Supabase to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpSupabase + LlamaIndex FAQ
Common questions about integrating Supabase 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 Supabase 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 Supabase to LlamaIndex
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.