Supabase Vector MCP Server for LlamaIndex 7 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Supabase Vector as an MCP tool provider through the 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 Supabase Vector. "
"You have 7 tools available."
),
)
response = await agent.run(
"What tools are available in Supabase Vector?"
)
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 Vector MCP Server
Integrate the powerful AI-native PostgreSQL extensions of Supabase Vector straight into your conversational LLM workflows. By authenticating your environment natively with the service_role key, your AI assistant bypasses row-level security constraints to operate as an unrestricted database administrator. Perform advanced similarity searches using the pgvector extension, parse and manipulate multi-dimensional embeddings, and execute foundational CRUD operations via simple natural language commands. Streamline RAG (Retrieval-Augmented Generation) setups and semantic engineering directly, avoiding the need for external dashboards or manual SQL querying.
LlamaIndex agents combine Supabase Vector tool responses with indexed documents for comprehensive, grounded answers. Connect 7 tools through the 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
- Semantic Vector Matching — Seamlessly query unstructured contextual similarities performing embedding comparisons by executing
match_vectorsutilizing custom postgres RPC parameters locally. - Database Structural Interaction — Systematically browse schema availability utilizing
list_tablesand extract specific data arrays effortlessly throughquery_table_rows. - Content State Manipulations — Seamlessly orchestrate data inputs invoking
insert_table_rowsor explicitly clear legacy assignments logically mapping identifiers withdelete_table_rows. - Custom Functional Logic — Launch sophisticated PL/pgSQL algorithms statically configured in your Supabase backend directly with
call_postgres_function.
The Supabase Vector MCP Server exposes 7 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 Vector to LlamaIndex via MCP
Follow these steps to integrate the Supabase Vector 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 7 tools from Supabase Vector
Why Use LlamaIndex with the Supabase Vector MCP Server
LlamaIndex provides unique advantages when paired with Supabase Vector through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Supabase Vector tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Supabase Vector tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Supabase Vector, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Supabase Vector tools were called, what data was returned, and how it influenced the final answer
Supabase Vector + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Supabase Vector MCP Server delivers measurable value.
Hybrid search: combine Supabase Vector real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Supabase Vector 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 Vector for fresh data
Analytical workflows: chain Supabase Vector queries with LlamaIndex's data connectors to build multi-source analytical reports
Supabase Vector MCP Tools for LlamaIndex (7)
These 7 tools become available when you connect Supabase Vector to LlamaIndex via MCP:
call_postgres_function
Calls a custom Postgres function (RPC) with parameters
delete_table_rows
This action is irreversible. Deletes rows from a table based on a column value
get_table_row
Retrieves a specific row by matching a column value
insert_table_rows
Provide a JSON array of row objects. Inserts new rows into a specific table
list_tables
Lists all tables in the Supabase project
match_vectors
Requires a valid RPC function name and an embedding array. Performs a vector similarity search via Postgres RPC
query_table_rows
Provide table name and optional select/limit. Queries rows from a specific table
Example Prompts for Supabase Vector in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Supabase Vector immediately.
"Using the 'match_docs' vector RPC natively, analyze my embedding representation returning seamlessly the top 5 matches."
"Browse my schema directly to identify active vector tables and delete any legacy testing embeddings from 'test_docs' securely."
"Insert a new embedding natively calling `insert_table_rows` with the corresponding context efficiently."
Troubleshooting Supabase Vector MCP Server with LlamaIndex
Common issues when connecting Supabase Vector to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpSupabase Vector + LlamaIndex FAQ
Common questions about integrating Supabase Vector 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 Vector 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 Vector to LlamaIndex
Get your token, paste the configuration, and start using 7 tools in under 2 minutes. No API key management needed.