Supabase Vector MCP, Ready to Go
Connect your AI agents to Supabase Vector to perform pgvector similarity searches and manage relational data directly from your favorite AI client.
No credit card required. Experience the power of this integration risk-free.
Manage pgvector embeddings and relational data through a conversational interface.
Works with every AI agent you already use
…and any MCP-compatible client
How fast is the Supabase Vector MCP Server?
Average time for the server to become ready for requests over the last 11 days, measured until the initialize / tools/list handshake completes. Metrics are updated daily between 00:00 and 04:00 UTC. Create a free account, use this MCP on Vinkius Cloud, and connect it to your AI agent in seconds.
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What AI agents can do with Supabase Vector 7-tool pgvector search
Use these tools to perform similarity searches, manage table rows, and run custom Postgres functions via your AI agent.
Delete table rows
Removes specific rows from a table based on a column value.
Get table row
Fetches a single row from a table when you provide a specific column value.
Insert table rows
Adds new data to a table using a JSON array of objects.
List tables
Shows a list of all available tables in your Supabase project.
Match vectors
Runs a similarity search using a vector RPC and an embedding array.
Query table rows
Pulls rows from a table with optional filters or limits.
Call postgres function
Executes a custom Postgres RPC function with your specific parameters.
One MCP enables access. Vinkius turns MCPs into production-ready infrastructure.
You're looking at one of 5,700+ managed MCPs. The real value isn't the catalog. It's the control plane that secures, governs, audits, and manages every interaction between your agents and the tools they use.
No Shadow AI
Every agent action is visible, approved, and auditable. Nothing runs outside your governance.
Absolute agent control
Fine-grained permissions for every agent, MCP, and tool. Instantly revoke access and audit every execution.
Cost control per token
Spend broken down to the token, tool, and agent. Budgets and hard limits. No surprise invoices.
Managed & monitored infra
We operate the runtime, authentication, scaling, retries, and monitoring. Your team manages AI, not infrastructure.
Data protection, DLP by design
Sensitive data is filtered before reaching the model. Access is governed so agents receive only the information they're allowed to use.
Token optimization, real savings
Lower AI costs by delivering the right context instead of unnecessary tools. Better accuracy, faster responses, and fewer wasted tokens.
Supabase Vector MCP for pgvector Semantic Search
This is for the engineer who is tired of copy-pasting IDs between a spreadsheet and a SQL console, or the data scientist who needs to see if their embeddings actually make sense without building a full frontend.
AI Engineer
Testing embedding models and RAG accuracy without leaving the chat window.
Database Administrator
Managing schema changes and data integrity for vector-enabled apps.
Backend Developer
Quickly debugging production data and verifying RPC functions.
Frequently Asked Questions
Can the Supabase Vector MCP help me build a RAG system? +
Yes, it connects your AI client directly to your vector database, making it easy to perform similarity searches and retrieve relevant context for your agent.
Does the Supabase Vector MCP allow me to delete data? +
It can delete specific rows from your tables based on a value you provide, which is great for cleaning up test data or removing old records.
Can I run my own custom functions with the Supabase Vector MCP? +
Yes, you can call any Postgres RPC functions you've already configured in your Supabase backend directly through your AI client.
Is this MCP only for vector searches? +
No, while it's great for pgvector similarity searches, it also lets your agent perform standard relational queries and manage your regular database tables.
Do I need to know SQL to use the Supabase Vector MCP? +
You don't need to write SQL; you just tell your AI client what you want to do in plain English, and it uses the MCP tools to execute the commands.
How does the Supabase Vector MCP handle my database security? +
It uses your service role key to operate as an administrator, so it can bypass row-level security to perform the actions you request.
Are embedding arrays processed efficiently during intensive vector similarity matching? +
The integration specifically manages large semantic arrays seamlessly by calling lightweight Postgres RPC configurations locally natively internally securely.
How is risk managed securely when manipulating and clearing root analytical vectors? +
Executing delete_table_rows operates systematically relying inherently on exactly structured string conditions implicitly naturally precisely eliminating ambiguity securely effectively actively strictly smoothly securely precisely correctly reliably locally dynamically successfully effortlessly intelligently gracefully elegantly safely accurately directly comprehensively natively.
Which distance metrics does the vector search support? +
pgvector supports cosine similarity, inner product, and L2 (Euclidean) distance. The metric used depends on how your RPC function and index are configured in PostgreSQL — the AI passes arguments accordingly.
Your AI, connected to everything.
No credit card required · Free tier available
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