What can @supabase/mcp-server-supabase do?

postgresql table crud operations via mcp protocol, real-time database change subscriptions via mcp resources, authentication and authorization context propagation, supabase storage file operations (upload, download, delete), vector similarity search via pgvector integration, edge function invocation and result streaming, schema introspection and capability discovery, transaction management and multi-step operations

@supabase/mcp-server-supabase

MCP ServerFree

MCP server for interacting with Supabase

Open Source

/ 100

8 capabilities

Capabilities8 decomposed

postgresql table crud operations via mcp protocol

Medium confidence

Exposes Supabase PostgreSQL tables as MCP resources with standardized read, create, update, and delete operations. Implements a schema-aware abstraction layer that translates MCP tool calls into parameterized SQL queries, handling type coercion and constraint validation at the protocol boundary. Uses Supabase's JavaScript client library to maintain connection pooling and authentication state.

Solves for

I want to query and modify database records from an AI agent without writing raw SQLI need to expose my Supabase schema to Claude or other MCP-compatible clients for autonomous data operationsI want type-safe database mutations through a standardized protocol instead of direct SQL injection

Best for

AI agent developers building autonomous workflows against Supabase backends

Teams integrating Supabase with Claude via MCP for data-driven decision making

Developers wanting to sandbox database access through protocol-level constraints

Requires

Supabase project with PostgreSQL database

Valid Supabase API key (service role or anon key with appropriate RLS policies)

Node.js 18+ runtime

Limitations

No built-in query optimization — complex joins or aggregations may require custom SQL tools

Row-level security (RLS) policies are enforced at the Supabase level but not explicitly surfaced in capability descriptions

Batch operations are sequential, not bulk-optimized — each mutation is a separate transaction

What makes it unique

Bridges MCP protocol semantics directly to Supabase's JavaScript client, avoiding raw SQL exposure while maintaining schema awareness through Supabase's introspection APIs. Implements request/response translation at the protocol layer rather than requiring custom tool definitions per table.

vs alternatives

Simpler than building custom OpenAI function schemas for each table, and more secure than exposing raw SQL execution to LLMs, because it enforces schema contracts through the MCP protocol itself.

real-time database change subscriptions via mcp resources

Medium confidence

Exposes Supabase Realtime subscriptions as MCP resources, allowing MCP clients to subscribe to PostgreSQL table changes (INSERT, UPDATE, DELETE) and receive streaming notifications. Implements WebSocket connection management through Supabase's Realtime client, translating change events into MCP resource updates that clients can poll or stream.

Solves for

I want an AI agent to react to database changes in real-time without pollingI need to stream live data updates from Supabase to a Claude session for context-aware responsesI want to trigger agent workflows when specific database mutations occur

Best for

Real-time AI agents that need to respond to live data changes

Collaborative applications where multiple agents need synchronized state

Event-driven workflows triggered by database mutations

Requires

Supabase Realtime service enabled on the project

Supabase API key with Realtime permissions

MCP client with resource subscription support

Limitations

Realtime subscriptions require Supabase Realtime service to be enabled and configured

WebSocket connections are stateful — reconnection logic must handle network interruptions

Change events include only the delta, not full row context — requires additional queries for full record state

What makes it unique

Leverages Supabase's native Realtime service (built on Elixir/Phoenix) rather than polling, reducing latency to sub-100ms for change notifications. Integrates WebSocket lifecycle management directly into MCP resource semantics, allowing clients to subscribe/unsubscribe through standard MCP calls.

vs alternatives

More efficient than polling-based alternatives because it uses server-push semantics; more integrated than generic webhook solutions because it maintains stateful subscriptions within the MCP session.

authentication and authorization context propagation

Medium confidence

Manages Supabase authentication tokens and row-level security (RLS) context within MCP tool execution. Implements token refresh logic and passes user identity through to PostgreSQL via Supabase's JWT claims, ensuring database operations respect RLS policies defined at the table/row level. Handles both service-role (unrestricted) and user-scoped (RLS-enforced) authentication modes.

Solves for

I want database operations to respect my Supabase RLS policies when executed by an AI agentI need to execute queries as a specific user identity, not as a service accountI want to ensure an agent cannot access rows it shouldn't, even if it tries to query them

Best for

Multi-tenant applications where agents must respect user isolation

Security-sensitive workflows where RLS enforcement is non-negotiable

Teams using Supabase's built-in authorization model

Requires

Supabase project with RLS policies defined on tables

Valid user session token or service role key

Supabase JWT configuration (default or custom claims)

Limitations

RLS policies are evaluated at the database level — agent cannot see policy definitions to reason about access

Token refresh requires valid refresh token; expired sessions will fail without re-authentication

Service-role mode bypasses RLS entirely — must be used carefully and only for trusted operations

What makes it unique

Propagates Supabase JWT claims directly into PostgreSQL session context via the `Authorization` header, allowing RLS policies to evaluate user identity at query time. Implements token lifecycle management (refresh, expiry) within the MCP server, not delegating to the client.

vs alternatives

More secure than application-level filtering because RLS is enforced at the database layer; more integrated than generic auth middleware because it uses Supabase's native JWT and claims model.

supabase storage file operations (upload, download, delete)

Medium confidence

Exposes Supabase Storage buckets as MCP resources with file management capabilities. Implements multipart upload handling for large files, signed URL generation for secure access, and metadata tracking. Uses Supabase's Storage API client to abstract S3-compatible operations, handling bucket policies and public/private access control.

Solves for

I want an AI agent to upload files to Supabase Storage without exposing storage keysI need to generate signed URLs for temporary file access from an agent workflowI want to manage file metadata and access control through MCP tools

Best for

Agents that need to persist generated content (documents, images, logs) to cloud storage

Multi-user applications where file access must be controlled per user

Workflows that generate and share files with expiring access windows

Requires

Supabase Storage bucket created and configured

Supabase API key with Storage permissions

File size within Storage tier limits (typically 5GB per file)

Limitations

File size limits depend on Supabase Storage tier — no built-in chunking for very large files

Signed URLs have fixed expiry times — cannot be extended after generation

No built-in virus scanning or content validation — must be implemented separately

What makes it unique

Integrates Supabase Storage's S3-compatible API with MCP semantics, providing bucket-level isolation and signed URL generation without exposing raw storage credentials. Handles multipart uploads transparently, abstracting S3 complexity from the MCP client.

vs alternatives

Simpler than direct S3 integration because it uses Supabase's managed buckets and RLS-compatible access control; more secure than exposing storage keys to agents because it uses signed URLs with time-limited access.

vector similarity search via pgvector integration

Medium confidence

Exposes Supabase's pgvector extension as MCP tools for semantic search and similarity queries. Implements vector embedding storage in PostgreSQL and provides cosine/L2 distance-based search through MCP tool calls. Integrates with embedding providers (OpenAI, Hugging Face) or accepts pre-computed embeddings, storing them in vector columns and querying via SQL operators.

Solves for

I want an AI agent to search my knowledge base using semantic similarity, not keyword matchingI need to find similar documents or records based on embedding distanceI want to build a RAG system where the agent can retrieve relevant context from a vector index

Best for

RAG (Retrieval-Augmented Generation) systems where agents need semantic search

Knowledge base applications with similarity-based retrieval

Recommendation systems that rank items by embedding distance

Requires

Supabase PostgreSQL database with pgvector extension enabled

Vector columns created in target tables with fixed dimensions

Pre-computed embeddings (from OpenAI, Hugging Face, or custom model)

Limitations

pgvector queries require pre-computed embeddings — no on-the-fly embedding generation within the query

Vector dimension must be fixed at table creation time — cannot change embedding model without schema migration

Large vector indexes (millions of rows) may require HNSW indexing for performance, adding complexity

What makes it unique

Leverages PostgreSQL's native pgvector extension for vector operations, avoiding external vector databases and keeping embeddings co-located with relational data. Implements similarity search through standard SQL, enabling hybrid queries that combine vector distance with traditional WHERE clauses.

vs alternatives

More integrated than separate vector databases (Pinecone, Weaviate) because vectors live in the same PostgreSQL instance as relational data; more flexible than embedding-only services because it supports arbitrary metadata filtering alongside similarity search.

edge function invocation and result streaming

Medium confidence

Exposes Supabase Edge Functions as MCP tools, allowing agents to invoke serverless functions deployed on Supabase's edge network. Implements HTTP request/response translation through the MCP protocol, handling function authentication, timeout management, and streaming responses. Supports both synchronous calls and long-running operations with status polling.

Solves for

I want an agent to trigger custom business logic without embedding it in the agent itselfI need to invoke serverless functions that run close to users (edge computing)I want to extend agent capabilities with custom code deployed separately from the MCP server

Best for

Teams with custom business logic deployed as Edge Functions

Workflows requiring low-latency execution (geographically distributed)

Agents that need to call external services through a Supabase-hosted proxy

Requires

Supabase Edge Functions deployed in the project

Valid Supabase API key with Edge Function invocation permissions

Function endpoint URL and authentication method

Limitations

Edge Function execution time is limited (typically 60-120 seconds) — long-running tasks must be async

Cold starts add 100-500ms latency on first invocation — not suitable for latency-critical operations

Function authentication requires valid Supabase API key — cannot be called anonymously without explicit configuration

What makes it unique

Wraps Supabase Edge Functions (Deno-based serverless) as MCP tools, translating HTTP semantics into the MCP protocol. Handles authentication and timeout management transparently, allowing agents to invoke functions without knowing HTTP details.

vs alternatives

More integrated than generic HTTP tools because it uses Supabase's native authentication and edge network; more flexible than embedding all logic in the MCP server because functions can be deployed and updated independently.

schema introspection and capability discovery

Medium confidence

Automatically discovers Supabase database schema (tables, columns, types, relationships) and exposes them as MCP resource definitions. Implements schema caching with optional refresh, generating tool descriptions and parameter schemas dynamically from PostgreSQL information_schema. Enables agents to understand available data structures without hardcoded tool definitions.

Solves for

I want an agent to understand my database schema without manual configurationI need to dynamically generate MCP tools based on my current database structureI want schema changes to automatically propagate to agent capabilities

Best for

Rapidly evolving schemas where manual tool definition is impractical

Multi-tenant systems with per-tenant schema variations

Teams wanting agents to adapt to schema changes without redeployment

Requires

Supabase API key with schema introspection permissions

PostgreSQL database with accessible information_schema

MCP client capable of dynamic tool discovery

Limitations

Schema introspection adds startup latency (typically 500ms-2s depending on schema size)

Complex types (custom PostgreSQL types, arrays) may not map cleanly to MCP parameter schemas

Schema caching can become stale if tables are modified outside the MCP server

What makes it unique

Queries PostgreSQL information_schema to generate MCP tool definitions at runtime, avoiding hardcoded tool lists. Implements schema caching with optional refresh, balancing startup performance against schema staleness.

vs alternatives

More maintainable than manual tool definition because schema changes are reflected automatically; more flexible than static tool lists because it adapts to per-tenant or per-environment schema variations.

transaction management and multi-step operations

Medium confidence

Provides MCP tools for managing PostgreSQL transactions, allowing agents to group multiple database operations into atomic units. Implements transaction lifecycle management (BEGIN, COMMIT, ROLLBACK) through MCP calls, with support for savepoints and isolation level configuration. Ensures consistency for complex workflows that require all-or-nothing semantics.

Solves for

I want an agent to execute multiple database operations atomically, rolling back if any step failsI need to ensure data consistency when an agent modifies related records across tablesI want to implement complex workflows with savepoints for partial rollback

Best for

Financial or transactional workflows where consistency is critical

Multi-step agent operations that must succeed or fail together

Systems requiring audit trails of atomic changes

Requires

Supabase PostgreSQL database

Supabase API key with transaction permissions

MCP client capable of stateful transaction management

Limitations

Long-running transactions hold locks, potentially blocking other operations — agents must minimize transaction duration

Nested transactions require savepoint management — complex for agents to reason about

Isolation level configuration is static per transaction — cannot change mid-transaction

What makes it unique

Exposes PostgreSQL transaction semantics (ACID guarantees, savepoints, isolation levels) through MCP tools, allowing agents to reason about consistency without raw SQL. Implements transaction state tracking within the MCP server to prevent accidental commits or rollbacks.

vs alternatives

More reliable than application-level consistency checks because it leverages PostgreSQL's ACID guarantees; more explicit than implicit transactions because agents can see and control transaction boundaries.

Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.

Related Artifactssharing capabilities

Artifacts that share capabilities with @supabase/mcp-server-supabase, ranked by overlap. Discovered automatically through the match graph.

MCP Server24

SchemaCrawler

** - Connect to any relational database, and be able to get valid SQL, and ask questions like what does a certain column prefix mean.

mcp-protocol-database-resource-exposuredatabase-schema-introspection-via-mcp

2 shared capabilities

MCP Server26

@benborla29/mcp-server-mysql

MCP server for interacting with MySQL databases with write operations support

mcp protocol server implementation for mysqlschema-based mysql query execution via mcp protocol

2 shared capabilities

MCP Server24

user-postgresql-mcp

A PostgreSQL MCP server built with @modelcontextprotocol/sdk.

postgresql query execution via mcp protocolmcp protocol message handling and routing

2 shared capabilities

MCP Server28

@iflow-mcp/garethcott_enhanced-postgres-mcp-server

Enhanced PostgreSQL MCP server with read and write capabilities. Based on @modelcontextprotocol/server-postgres by Anthropic.

postgresql query execution via mcp protocolmcp resource-based database schema introspection

2 shared capabilities

MCP Server32

mysql_mcp_server

A Model Context Protocol (MCP) server that enables secure interaction with MySQL databases

mcp-compliant database resource discovery and enumerationmcp protocol server implementation with stdio transport

2 shared capabilities

MCP Server28

Teradata

** - A collection of tools for managing the platform, addressing data quality and reading and writing to [Teradata](https://www.teradata.com/) Database.

mcp protocol bridging with multi-transport support

1 shared capability

Best For

✓AI agent developers building autonomous workflows against Supabase backends
✓Teams integrating Supabase with Claude via MCP for data-driven decision making
✓Developers wanting to sandbox database access through protocol-level constraints
✓Real-time AI agents that need to respond to live data changes
✓Collaborative applications where multiple agents need synchronized state
✓Event-driven workflows triggered by database mutations
✓Multi-tenant applications where agents must respect user isolation
✓Security-sensitive workflows where RLS enforcement is non-negotiable

Known Limitations

⚠No built-in query optimization — complex joins or aggregations may require custom SQL tools
⚠Row-level security (RLS) policies are enforced at the Supabase level but not explicitly surfaced in capability descriptions
⚠Batch operations are sequential, not bulk-optimized — each mutation is a separate transaction
⚠No support for stored procedures or custom functions through the standard CRUD interface
⚠Realtime subscriptions require Supabase Realtime service to be enabled and configured
⚠WebSocket connections are stateful — reconnection logic must handle network interruptions

Requirements

Supabase project with PostgreSQL databaseValid Supabase API key (service role or anon key with appropriate RLS policies)Node.js 18+ runtimeMCP-compatible client (Claude, or custom MCP host)Supabase Realtime service enabled on the projectSupabase API key with Realtime permissionsMCP client with resource subscription supportNetwork connectivity for WebSocket (not available in restricted environments)

Input / Output

Accepts: JSON objects matching table schema, Filter/where clause objects, Column selection arrays, Table name and schema, Event filter (INSERT, UPDATE, DELETE), Optional row-level filters, User JWT token or API key, User ID or custom claims, Authentication mode flag (user vs service-role), File binary data or stream, File path and bucket name, Metadata (content-type, cache-control), Access control flags (public/private), Query vector (array of floats), Similarity metric (cosine, L2, inner product), Result limit and threshold, Optional metadata filters, Function name or URL, Request body (JSON or binary), HTTP method and headers, Timeout configuration, Schema refresh trigger, Optional schema filter (specific tables or schemas), Transaction control commands (BEGIN, COMMIT, ROLLBACK, SAVEPOINT), Isolation level (READ UNCOMMITTED, READ COMMITTED, REPEATABLE READ, SERIALIZABLE), Optional savepoint name

Produces: JSON rows from tables, Operation status (success/error), Row counts for mutations, Change event objects with operation type and affected rows, Streaming notifications, Connection status updates, Authenticated request context, RLS-filtered query results, Authorization error messages, File URL or signed URL, Upload/download status, File metadata (size, created_at, updated_at), Ranked list of similar records with distance scores, Metadata and content of matched rows, Similarity scores (0-1 for cosine), Function response body (JSON, text, or binary), HTTP status code, Response headers, Execution time metadata, MCP tool definitions with parameter schemas, Table and column metadata, Relationship information (foreign keys), Transaction status (active, committed, rolled back), Error messages on transaction failure, Savepoint confirmation

UnfragileRank

Adoption58%(30% weight)

Quality17%(25% weight)

Ecosystem30%(25% weight)

Match Graph10%(15% weight)

Freshness75%(5% weight)

UnfragileRank is computed from adoption signals, documentation quality, ecosystem connectivity, match graph feedback, and freshness. No artifact can pay for a higher rank.

Type: MCP Server

8 capabilities

Visit @supabase/mcp-server-supabase→

Package Details

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0.7.0

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MCP server for interacting with Supabase

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Capabilities8 decomposed

postgresql table crud operations via mcp protocol

Medium confidence

Solves for

Best for

AI agent developers building autonomous workflows against Supabase backends

Teams integrating Supabase with Claude via MCP for data-driven decision making

Developers wanting to sandbox database access through protocol-level constraints

Requires

Supabase project with PostgreSQL database

Valid Supabase API key (service role or anon key with appropriate RLS policies)

Node.js 18+ runtime

Limitations

No built-in query optimization — complex joins or aggregations may require custom SQL tools

Row-level security (RLS) policies are enforced at the Supabase level but not explicitly surfaced in capability descriptions

Batch operations are sequential, not bulk-optimized — each mutation is a separate transaction

What makes it unique

vs alternatives

Simpler than building custom OpenAI function schemas for each table, and more secure than exposing raw SQL execution to LLMs, because it enforces schema contracts through the MCP protocol itself.

real-time database change subscriptions via mcp resources

Medium confidence

Solves for

Best for

Real-time AI agents that need to respond to live data changes

Collaborative applications where multiple agents need synchronized state

Event-driven workflows triggered by database mutations

Requires

Supabase Realtime service enabled on the project

Supabase API key with Realtime permissions

MCP client with resource subscription support

Limitations

Realtime subscriptions require Supabase Realtime service to be enabled and configured

WebSocket connections are stateful — reconnection logic must handle network interruptions

Change events include only the delta, not full row context — requires additional queries for full record state

What makes it unique

vs alternatives

authentication and authorization context propagation

Medium confidence

Solves for

Best for

Multi-tenant applications where agents must respect user isolation

Security-sensitive workflows where RLS enforcement is non-negotiable

Teams using Supabase's built-in authorization model

Requires

Supabase project with RLS policies defined on tables

Valid user session token or service role key

Supabase JWT configuration (default or custom claims)

Limitations

RLS policies are evaluated at the database level — agent cannot see policy definitions to reason about access

Token refresh requires valid refresh token; expired sessions will fail without re-authentication

Service-role mode bypasses RLS entirely — must be used carefully and only for trusted operations

What makes it unique

vs alternatives

More secure than application-level filtering because RLS is enforced at the database layer; more integrated than generic auth middleware because it uses Supabase's native JWT and claims model.

supabase storage file operations (upload, download, delete)

Medium confidence

Solves for

Best for

Agents that need to persist generated content (documents, images, logs) to cloud storage

Multi-user applications where file access must be controlled per user

Workflows that generate and share files with expiring access windows

Requires

Supabase Storage bucket created and configured

Supabase API key with Storage permissions

File size within Storage tier limits (typically 5GB per file)

Limitations

File size limits depend on Supabase Storage tier — no built-in chunking for very large files

Signed URLs have fixed expiry times — cannot be extended after generation

No built-in virus scanning or content validation — must be implemented separately

What makes it unique

vs alternatives

vector similarity search via pgvector integration

Medium confidence

Solves for

Best for

RAG (Retrieval-Augmented Generation) systems where agents need semantic search

Knowledge base applications with similarity-based retrieval

Recommendation systems that rank items by embedding distance

Requires

Supabase PostgreSQL database with pgvector extension enabled

Vector columns created in target tables with fixed dimensions

Pre-computed embeddings (from OpenAI, Hugging Face, or custom model)

Limitations

pgvector queries require pre-computed embeddings — no on-the-fly embedding generation within the query

Vector dimension must be fixed at table creation time — cannot change embedding model without schema migration

Large vector indexes (millions of rows) may require HNSW indexing for performance, adding complexity

What makes it unique

vs alternatives

edge function invocation and result streaming

Medium confidence

Solves for

Best for

Teams with custom business logic deployed as Edge Functions

Workflows requiring low-latency execution (geographically distributed)

Agents that need to call external services through a Supabase-hosted proxy

Requires

Supabase Edge Functions deployed in the project

Valid Supabase API key with Edge Function invocation permissions

Function endpoint URL and authentication method

Limitations

Edge Function execution time is limited (typically 60-120 seconds) — long-running tasks must be async

Cold starts add 100-500ms latency on first invocation — not suitable for latency-critical operations

Function authentication requires valid Supabase API key — cannot be called anonymously without explicit configuration

What makes it unique

vs alternatives

schema introspection and capability discovery

Medium confidence

Solves for

Best for

Rapidly evolving schemas where manual tool definition is impractical

Multi-tenant systems with per-tenant schema variations

Teams wanting agents to adapt to schema changes without redeployment

Requires

Supabase API key with schema introspection permissions

PostgreSQL database with accessible information_schema

MCP client capable of dynamic tool discovery

Limitations

Schema introspection adds startup latency (typically 500ms-2s depending on schema size)

Complex types (custom PostgreSQL types, arrays) may not map cleanly to MCP parameter schemas

Schema caching can become stale if tables are modified outside the MCP server

What makes it unique

vs alternatives

transaction management and multi-step operations

Medium confidence

Solves for

Best for

Financial or transactional workflows where consistency is critical

Multi-step agent operations that must succeed or fail together

Systems requiring audit trails of atomic changes

Requires

Supabase PostgreSQL database

Supabase API key with transaction permissions

MCP client capable of stateful transaction management

Limitations

Long-running transactions hold locks, potentially blocking other operations — agents must minimize transaction duration

Nested transactions require savepoint management — complex for agents to reason about

Isolation level configuration is static per transaction — cannot change mid-transaction

What makes it unique

vs alternatives

Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.

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@supabase/mcp-server-supabase

Capabilities8 decomposed

postgresql table crud operations via mcp protocol

real-time database change subscriptions via mcp resources

authentication and authorization context propagation

supabase storage file operations (upload, download, delete)

vector similarity search via pgvector integration

edge function invocation and result streaming

schema introspection and capability discovery

transaction management and multi-step operations

Related Artifactssharing capabilities

SchemaCrawler

@benborla29/mcp-server-mysql

user-postgresql-mcp

@iflow-mcp/garethcott_enhanced-postgres-mcp-server

mysql_mcp_server

Teradata

Best For

Known Limitations

Requirements

Input / Output

UnfragileRank

Package Details

About

Categories

Alternatives to @supabase/mcp-server-supabase

Are you the builder of @supabase/mcp-server-supabase?

Get the weekly brief

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@supabase/mcp-server-supabase

Capabilities8 decomposed

postgresql table crud operations via mcp protocol

real-time database change subscriptions via mcp resources

authentication and authorization context propagation

supabase storage file operations (upload, download, delete)

vector similarity search via pgvector integration

edge function invocation and result streaming

schema introspection and capability discovery

transaction management and multi-step operations

Related Artifactssharing capabilities

SchemaCrawler

@benborla29/mcp-server-mysql

user-postgresql-mcp

@iflow-mcp/garethcott_enhanced-postgres-mcp-server

mysql_mcp_server

Teradata

Best For

Known Limitations

Requirements

Input / Output

UnfragileRank

Package Details

About

Categories

Alternatives to @supabase/mcp-server-supabase

Are you the builder of @supabase/mcp-server-supabase?

Get the weekly brief

Data Sources