Supabase Vector MCP Server for Mastra AI 7 tools — connect in under 2 minutes
Mastra AI is a TypeScript-native agent framework built for modern web stacks. Connect Supabase Vector through the Vinkius and Mastra agents discover all tools automatically — type-safe, streaming-ready, and deployable anywhere Node.js runs.
ASK AI ABOUT THIS MCP SERVER
Vinkius supports streamable HTTP and SSE.
import { Agent } from "@mastra/core/agent";
import { createMCPClient } from "@mastra/mcp";
import { openai } from "@ai-sdk/openai";
async function main() {
// Your Vinkius token — get it at cloud.vinkius.com
const mcpClient = await createMCPClient({
servers: {
"supabase-vector": {
url: "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
},
},
});
const tools = await mcpClient.getTools();
const agent = new Agent({
name: "Supabase Vector Agent",
instructions:
"You help users interact with Supabase Vector " +
"using 7 tools.",
model: openai("gpt-4o"),
tools,
});
const result = await agent.generate(
"What can I do with Supabase Vector?"
);
console.log(result.text);
}
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.
Mastra's agent abstraction provides a clean separation between LLM logic and Supabase Vector tool infrastructure. Connect 7 tools through the Vinkius and use Mastra's built-in workflow engine to chain tool calls with conditional logic, retries, and parallel execution — deployable to any Node.js host in one command.
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 Mastra AI 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 Mastra AI via MCP
Follow these steps to integrate the Supabase Vector MCP Server with Mastra AI.
Install dependencies
Run npm install @mastra/core @mastra/mcp @ai-sdk/openai
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token
Run the agent
Save to agent.ts and run with npx tsx agent.ts
Explore tools
Mastra discovers 7 tools from Supabase Vector via MCP
Why Use Mastra AI with the Supabase Vector MCP Server
Mastra AI provides unique advantages when paired with Supabase Vector through the Model Context Protocol.
Mastra's agent abstraction provides a clean separation between LLM logic and tool infrastructure — add Supabase Vector without touching business code
Built-in workflow engine chains MCP tool calls with conditional logic, retries, and parallel execution for complex automation
TypeScript-native: full type inference for every Supabase Vector tool response with IDE autocomplete and compile-time checks
One-command deployment to any Node.js host — Vercel, Railway, Fly.io, or your own infrastructure
Supabase Vector + Mastra AI Use Cases
Practical scenarios where Mastra AI combined with the Supabase Vector MCP Server delivers measurable value.
Automated workflows: build multi-step agents that query Supabase Vector, process results, and trigger downstream actions in a typed pipeline
SaaS integrations: embed Supabase Vector as a first-class tool in your product's AI features with Mastra's clean agent API
Background jobs: schedule Mastra agents to query Supabase Vector on a cron and store results in your database automatically
Multi-agent systems: create specialist agents that collaborate using Supabase Vector tools alongside other MCP servers
Supabase Vector MCP Tools for Mastra AI (7)
These 7 tools become available when you connect Supabase Vector to Mastra AI 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 Mastra AI
Ready-to-use prompts you can give your Mastra AI 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 Mastra AI
Common issues when connecting Supabase Vector to Mastra AI through the Vinkius, and how to resolve them.
createMCPClient not exported
npm install @mastra/mcpSupabase Vector + Mastra AI FAQ
Common questions about integrating Supabase Vector MCP Server with Mastra AI.
How does Mastra AI connect to MCP servers?
MCPClient with the server URL and pass it to your agent. Mastra discovers all tools and makes them available with full TypeScript types.Can Mastra agents use tools from multiple servers?
Does Mastra support workflow orchestration?
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 Mastra AI
Get your token, paste the configuration, and start using 7 tools in under 2 minutes. No API key management needed.