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The MCP server translates query requests into native PostgreSQL operations, handling connection pooling, authentication via API keys, and result serialization back to the client. Supports parameterized queries to prevent SQL injection and returns structured result sets with column metadata.","intents":["Execute raw SQL queries against my Supabase database from an AI agent or LLM","Run complex analytical queries or data transformations without building a custom API","Perform database operations (INSERT, UPDATE, DELETE, SELECT) programmatically through MCP"],"best_for":["AI agents and LLM applications needing direct database access","Developers building data-driven workflows that require SQL-level control","Teams automating database operations through Claude or other MCP-compatible clients"],"limitations":["No built-in query optimization or explain plan analysis — complex queries may timeout","Result sets are fully materialized in memory before serialization, limiting very large result handling","No transaction support across multiple MCP calls — each query is isolated","Query execution time depends on Supabase connection latency and database load"],"requires":["Supabase project with active PostgreSQL database","Supabase API key (anon or service role key)","Supabase project URL","Network connectivity to Supabase endpoints"],"input_types":["SQL query string","Query parameters (optional, for parameterized queries)"],"output_types":["JSON array of result rows","Column metadata (names, types)","Row count and affected rows"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-supabase-mcp-server-supabase__cap_1","uri":"capability://data.processing.analysis.table.schema.introspection.and.metadata.retrieval","name":"table schema introspection and metadata retrieval","description":"Retrieves comprehensive schema information from Supabase PostgreSQL tables including column definitions, data types, constraints, indexes, and relationships. The MCP server queries PostgreSQL system catalogs (information_schema) to extract metadata without requiring manual schema definitions. Results include column nullability, default values, foreign key relationships, and primary key information.","intents":["Understand the structure of my Supabase database tables before writing queries","Generate type definitions or ORM models from live database schema","Validate data before insertion by checking column constraints and types","Discover relationships between tables for JOIN operations"],"best_for":["AI agents that need to understand database structure before generating SQL","Developers building schema-aware code generation tools","Teams automating database documentation or type generation"],"limitations":["Metadata retrieval is read-only — cannot modify schema through this capability","Does not include custom comments or documentation stored in PostgreSQL","Performance depends on database size; very large schemas may have slower introspection","Does not expose RLS (Row Level Security) policies or their conditions"],"requires":["Supabase project with PostgreSQL database","Supabase API key with read access to information_schema","Supabase project URL"],"input_types":["Table name (optional — can list all tables)","Schema name (defaults to 'public')"],"output_types":["JSON schema definition with column metadata","Table relationships and foreign keys","Index definitions","Constraint information"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-supabase-mcp-server-supabase__cap_2","uri":"capability://automation.workflow.real.time.database.change.subscriptions.via.supabase.realtime","name":"real-time database change subscriptions via supabase realtime","description":"Establishes WebSocket subscriptions to Supabase Realtime to receive live notifications when database records are inserted, updated, or deleted. The MCP server manages subscription lifecycle, filters changes by table and optional WHERE conditions, and streams change events to connected clients. Leverages Supabase's built-in Realtime infrastructure which uses PostgreSQL LISTEN/NOTIFY under the hood.","intents":["Subscribe to live database changes and react to them in real-time workflows","Build event-driven agents that respond to database mutations","Monitor specific tables or rows for changes without polling"],"best_for":["AI agents that need to react to live database events","Event-driven automation workflows triggered by database changes","Real-time collaborative applications requiring change notifications"],"limitations":["Realtime subscriptions require WebSocket support — not available in all network environments","Change events are delivered at-least-once but not guaranteed in strict order for high-throughput scenarios","Supabase Realtime has rate limits and connection limits per project","Cannot subscribe to changes across multiple databases or external data sources"],"requires":["Supabase project with Realtime enabled","Supabase API key (anon or service role)","WebSocket connectivity to Supabase Realtime endpoint","MCP client with streaming/subscription support"],"input_types":["Table name","Event type filter (INSERT, UPDATE, DELETE, or '*')","Optional WHERE clause for row-level filtering"],"output_types":["Real-time change events (JSON)","Event metadata (timestamp, operation type, old/new values)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-supabase-mcp-server-supabase__cap_3","uri":"capability://tool.use.integration.authentication.and.user.management.via.supabase.auth","name":"authentication and user management via supabase auth","description":"Manages Supabase Auth operations including user creation, password resets, email verification, and session management. The MCP server interfaces with Supabase Auth API to handle authentication flows, token generation, and user metadata updates. Supports both email/password and OAuth provider authentication, with built-in handling of JWT tokens and refresh token rotation.","intents":["Create and manage user accounts programmatically through an AI agent","Trigger password reset flows or email verification workflows","Retrieve user information and manage user metadata","Implement custom authentication flows in automated systems"],"best_for":["AI agents managing user lifecycle in Supabase applications","Automated onboarding or user provisioning workflows","Teams building custom authentication UIs or flows"],"limitations":["OAuth flows require user interaction (redirect) — not suitable for fully automated scenarios","Session management is stateless (JWT-based) — no server-side session revocation without additional setup","Email verification and password reset require email delivery — depends on Supabase email configuration","Cannot directly access user passwords or perform password validation without hashing"],"requires":["Supabase project with Auth enabled","Supabase API key (service role key for admin operations)","Supabase project URL","Email provider configured (for password reset and verification emails)"],"input_types":["User email and password","User metadata (JSON)","OAuth provider credentials"],"output_types":["User object with ID and metadata","JWT access token and refresh token","Session information"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-supabase-mcp-server-supabase__cap_4","uri":"capability://tool.use.integration.file.storage.operations.via.supabase.storage","name":"file storage operations via supabase storage","description":"Manages file uploads, downloads, and deletions in Supabase Storage buckets through the MCP server. The MCP server translates file operation requests into Supabase Storage API calls, handling multipart uploads, signed URLs for secure access, and bucket-level access control. Supports both public and private buckets with configurable retention and access policies.","intents":["Upload files to Supabase Storage from an AI agent or automated workflow","Generate signed URLs for secure file access without exposing storage credentials","Delete or manage files in storage buckets programmatically","Retrieve file metadata and list bucket contents"],"best_for":["AI agents that need to store generated content (documents, images, etc.)","Automated workflows requiring file management without direct cloud storage access","Applications needing secure, temporary file access through signed URLs"],"limitations":["File size limits depend on Supabase plan — large files may require chunked uploads","Signed URLs have configurable expiration — cannot create permanent public URLs through this capability","No built-in virus scanning or content validation — must be implemented separately","Storage operations are asynchronous — large uploads may timeout if not properly managed"],"requires":["Supabase project with Storage enabled","Supabase API key (anon or service role)","Storage bucket created in Supabase","Appropriate bucket policies configured"],"input_types":["File content (binary or text)","File path/name","Bucket name","File metadata (MIME type, etc.)"],"output_types":["File URL or signed URL","File metadata (size, created date)","Bucket contents listing"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-supabase-mcp-server-supabase__cap_5","uri":"capability://search.retrieval.vector.similarity.search.via.supabase.pgvector.extension","name":"vector similarity search via supabase pgvector extension","description":"Performs semantic similarity searches using PostgreSQL pgvector extension integrated with Supabase. The MCP server accepts vector embeddings (typically from an LLM embedding model) and executes similarity queries using cosine distance, L2 distance, or inner product operators. Results are ranked by similarity score and can be filtered by additional SQL conditions for hybrid search.","intents":["Search for semantically similar documents or records using vector embeddings","Build RAG (Retrieval-Augmented Generation) systems that retrieve relevant context","Implement semantic search without external vector database","Perform hybrid search combining vector similarity with keyword filtering"],"best_for":["AI agents building RAG pipelines with Supabase as the vector store","Teams implementing semantic search without managing separate vector databases","LLM applications needing efficient similarity search over embeddings"],"limitations":["pgvector performance degrades with very large embedding collections (millions+) — may require partitioning","Vector dimensions must match across all queries — no automatic dimension handling","No built-in embedding generation — requires external embedding model or API","Similarity search results are not automatically deduplicated or ranked by relevance beyond distance metric"],"requires":["Supabase project with pgvector extension enabled","PostgreSQL table with vector column (pgvector type)","Pre-computed embeddings stored in the vector column","Embedding model to generate query vectors"],"input_types":["Query vector (array of floats)","Similarity metric (cosine, L2, inner product)","Result limit","Optional SQL WHERE clause for filtering"],"output_types":["Ranked result set with similarity scores","Original record data with distance/similarity metric"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-supabase-mcp-server-supabase__cap_6","uri":"capability://safety.moderation.row.level.security.rls.policy.evaluation.and.enforcement","name":"row-level security (rls) policy evaluation and enforcement","description":"Enforces Supabase Row-Level Security policies by evaluating RLS rules before executing queries. The MCP server applies RLS policies based on the authenticated user's claims (from JWT tokens) and filters query results accordingly. Policies are defined in PostgreSQL and automatically enforced at the database level, preventing unauthorized data access.","intents":["Ensure database queries respect RLS policies for multi-tenant applications","Automatically filter results based on user permissions without manual WHERE clauses","Implement fine-grained access control at the database level"],"best_for":["Multi-tenant SaaS applications requiring per-user data isolation","Teams building applications with complex authorization requirements","AI agents that need to respect user permissions when querying data"],"limitations":["RLS policies are defined in PostgreSQL — cannot be modified through MCP without direct database access","RLS enforcement adds query overhead — complex policies may impact performance","Debugging RLS policy issues requires PostgreSQL logs and direct database inspection","RLS policies apply at query time — cannot be previewed or tested without executing queries"],"requires":["Supabase project with RLS enabled on tables","RLS policies defined in PostgreSQL","Authenticated user with valid JWT token","User claims (uid, email, etc.) embedded in JWT"],"input_types":["SQL query","User JWT token with claims"],"output_types":["Filtered result set respecting RLS policies","Query execution with RLS enforcement"],"categories":["safety-moderation","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-supabase-mcp-server-supabase__cap_7","uri":"capability://tool.use.integration.database.function.invocation.with.postgresql.stored.procedures","name":"database function invocation with postgresql stored procedures","description":"Executes PostgreSQL stored procedures and functions through the MCP server, enabling complex database logic encapsulation. The MCP server translates function calls into native PostgreSQL function invocations, handling parameter passing, return value serialization, and error handling. Supports functions with multiple parameters, return types (scalar, composite, or set-returning), and transaction semantics.","intents":["Call custom PostgreSQL functions to execute complex business logic at the database level","Invoke stored procedures for data transformation or aggregation","Execute database functions that require transaction semantics or complex logic"],"best_for":["Teams with existing PostgreSQL stored procedures wanting to expose them to AI agents","Applications requiring complex database logic that's more efficient in PostgreSQL","Workflows needing transactional guarantees for multi-step operations"],"limitations":["Stored procedures must be pre-defined in PostgreSQL — cannot create functions through MCP","Function signatures and return types must be known in advance for proper serialization","Debugging stored procedure logic requires PostgreSQL logs and direct database inspection","Performance depends on function complexity and database load"],"requires":["PostgreSQL stored procedures or functions defined in Supabase database","Supabase API key with execute permissions on functions","Knowledge of function signatures and parameter types"],"input_types":["Function name","Function parameters (typed according to PostgreSQL signature)"],"output_types":["Function return value (scalar, composite, or set-returning)","Execution status and any errors"],"categories":["tool-use-integration","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":41,"verified":false,"data_access_risk":"high","permissions":["Supabase project with active PostgreSQL database","Supabase API key (anon or service role key)","Supabase project URL","Network connectivity to Supabase endpoints","Supabase project with PostgreSQL database","Supabase API key with read access to information_schema","Supabase project with Realtime enabled","Supabase API key (anon or service role)","WebSocket connectivity to Supabase Realtime endpoint","MCP client with streaming/subscription support"],"failure_modes":["No built-in query optimization or explain plan analysis — complex queries may timeout","Result sets are fully materialized in memory before serialization, limiting very large result handling","No transaction support across multiple MCP calls — each query is isolated","Query execution time depends on Supabase connection latency and database load","Metadata retrieval is read-only — cannot modify schema through this capability","Does not include custom comments or documentation stored in PostgreSQL","Performance depends on database size; very large schemas may have slower introspection","Does not expose RLS (Row Level Security) policies or their conditions","Realtime subscriptions require WebSocket support — not available in all network environments","Change events are delivered at-least-once but not guaranteed in strict order for high-throughput scenarios","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.6704820443471833,"quality":0.26,"ecosystem":0.39999999999999997,"match_graph":0.25,"freshness":0.52,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.15,"match_graph":0.23,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:23.902Z","last_scraped_at":"2026-05-03T14:04:47.472Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":112559,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=supabase-mcp-server-supabase","compare_url":"https://unfragile.ai/compare?artifact=supabase-mcp-server-supabase"}},"signature":"CO7Wedq+87nszYSdOAhHWtLPF+G+DkdxiPUAldJ8LeSH6UUxyIGTzsuQg+1X0X3sMsohnwBqhNc1qThWPp6FDg==","signedAt":"2026-06-20T05:31:11.374Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/supabase-mcp-server-supabase","artifact":"https://unfragile.ai/supabase-mcp-server-supabase","verify":"https://unfragile.ai/api/v1/verify?slug=supabase-mcp-server-supabase","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}