| Ecosystem | 1 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 10 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Maintains a registry of multiple Strapi server endpoints with authentication credentials, allowing seamless context switching between different Strapi deployments without re-authentication. Implements connection pooling and credential caching to enable rapid server switching within a single MCP session, storing server configurations in a local state store that persists across tool invocations.
Unique: Implements server registry pattern within MCP stateful context, allowing persistent server switching without re-authentication — most MCP tools require fresh auth per session
vs alternatives: Eliminates context-switching friction compared to managing separate Strapi CLI sessions or manual API client configurations for each server
Queries Strapi's content-type API endpoints to dynamically discover and cache all available content types, components, and their field schemas. Exposes hierarchical navigation of content structure with field metadata (type, validation rules, relations), enabling intelligent prompting and validation without hardcoding schema knowledge. Uses Strapi's GraphQL introspection or REST schema endpoints to build an in-memory schema graph.
Unique: Dynamically builds schema graph from Strapi's content-type API rather than requiring manual schema definition, enabling zero-configuration schema awareness for any Strapi instance
vs alternatives: Provides real-time schema discovery vs static schema files or manual documentation, reducing schema drift and enabling adaptation to schema changes without code updates
Implements full CRUD operations (Create, Read, Update, Delete) against Strapi's REST API with automatic payload validation against discovered schemas. Constructs properly-formatted REST requests with correct endpoint routing, query parameters, and request bodies based on content type structure. Includes error handling for validation failures, permission errors, and API rate limits with automatic retry logic.
Unique: Validates payloads against dynamically discovered schemas before submission, preventing invalid API calls and providing immediate feedback on schema violations rather than server-side errors
vs alternatives: Reduces round-trip errors compared to raw REST clients by validating locally first, and provides schema-aware field mapping vs manual endpoint construction
Handles file uploads to Strapi's media library via multipart form-data requests with automatic MIME type detection and file validation. Supports batch uploads with progress tracking, implements resumable uploads for large files, and manages asset metadata (alt text, captions, focal points). Integrates with Strapi's upload plugin to handle provider-specific storage (local filesystem, S3, Cloudinary, etc.) transparently.
Unique: Abstracts provider-specific upload details (S3, Cloudinary, local) behind a unified interface, allowing asset uploads to work identically regardless of Strapi's configured storage backend
vs alternatives: Provides transparent multi-provider support vs provider-specific clients, and integrates upload with metadata management in a single operation vs separate API calls
Resolves and manages relationships between content entries (one-to-many, many-to-many, polymorphic relations) by tracking foreign keys and populating related content automatically. Implements lazy-loading of related entries with configurable depth to avoid circular reference issues. Provides bidirectional relationship management — updating a relation from either side maintains consistency.
Unique: Implements bidirectional relationship management with automatic consistency checking, ensuring that updating a relation from either side maintains referential integrity without manual synchronization
vs alternatives: Provides transparent relationship resolution vs manual foreign key management, and handles polymorphic relations vs simple foreign key systems
Constructs complex Strapi REST API queries with support for filtering, sorting, pagination, and field selection. Implements a query builder pattern that translates human-readable filter expressions into Strapi's query syntax (e.g., $eq, $ne, $gt, $contains). Supports nested filters on related content and full-text search capabilities where available. Automatically optimizes queries to minimize API calls and response payload size.
Unique: Translates natural language filter expressions into Strapi query syntax, allowing non-technical users to construct complex queries without learning API syntax
vs alternatives: Provides query builder abstraction vs raw REST API construction, and supports natural language filters vs requiring manual operator syntax
Manages content lifecycle states (draft, published, archived) with automatic timestamp tracking and version history. Implements publish scheduling to set future publication dates, and provides rollback capabilities to revert to previous versions. Handles draft-to-publish transitions with validation to ensure required fields are populated before publishing.
Unique: Integrates draft-and-publish state management with scheduling and rollback in a single capability, providing end-to-end content lifecycle control without separate tool invocations
vs alternatives: Provides scheduled publishing vs manual publish-on-demand, and automatic version tracking vs manual snapshot management
Handles Strapi's component and dynamic zone features, which allow flexible content composition with reusable field groups. Manages component instances within entries, supporting nested components and dynamic zone polymorphic content. Validates component payloads against component schemas and handles component lifecycle (create, update, delete within parent entry).
Unique: Provides unified interface for managing both reusable components and dynamic zone polymorphic content, handling the complexity of nested component validation and lifecycle
vs alternatives: Abstracts component complexity vs raw REST API calls, and validates component payloads locally vs server-side validation errors
+2 more capabilities
Executes SQL queries against Supabase PostgreSQL instances through the Model Context Protocol, translating natural language or structured query requests into parameterized SQL statements. Uses MCP's tool-calling interface to expose database operations as callable functions with schema validation, enabling LLM agents to perform CRUD operations, joins, and aggregations with automatic connection pooling and credential management through Supabase client SDK.
Unique: Exposes Supabase PostgreSQL as MCP tools with automatic credential injection from Supabase client SDK, eliminating manual connection string management and enabling seamless LLM-to-database queries within Claude or compatible agents
vs alternatives: Tighter integration than generic SQL MCP servers because it leverages Supabase's built-in authentication and connection pooling rather than requiring separate database credential configuration
Exposes Supabase Auth session state and user metadata through MCP tools, allowing agents to inspect current authentication context, retrieve user profiles, and trigger auth-related operations. Integrates with Supabase's JWT-based auth system to validate sessions and access user claims without re-authenticating, using the Supabase client's built-in session management.
Unique: Integrates Supabase's JWT-based auth system directly into MCP tool interface, allowing agents to inspect and act on auth state without managing separate credential stores or re-authentication flows
vs alternatives: More seamless than generic auth MCP servers because it leverages Supabase's built-in session management and avoids redundant credential passing between agent and auth system
Invokes Supabase Edge Functions (serverless TypeScript/JavaScript functions) through MCP tools, passing parameters and receiving results with optional streaming support. Uses Supabase's edge function HTTP API to trigger functions with automatic authentication headers and response parsing, enabling agents to execute custom business logic without embedding it in the agent itself.
Supabase scores higher at 42/100 vs Strapi Connector at 32/100.
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Unique: Exposes Supabase Edge Functions as MCP tools with automatic authentication and response parsing, allowing agents to invoke custom serverless logic without managing HTTP clients or credential injection
vs alternatives: More integrated than generic HTTP MCP tools because it handles Supabase-specific authentication, error handling, and response formatting automatically
Subscribes to real-time changes on Supabase tables through MCP's event streaming interface, using Supabase's PostgreSQL LISTEN/NOTIFY mechanism to push INSERT, UPDATE, and DELETE events to agents. Maintains persistent WebSocket connections and filters events by table and row-level policies, enabling agents to react to database changes without polling.
Unique: Bridges Supabase's PostgreSQL LISTEN/NOTIFY real-time system with MCP's tool interface, enabling agents to subscribe to database changes without managing WebSocket connections or event serialization
vs alternatives: More efficient than polling-based approaches because it uses Supabase's native real-time infrastructure rather than repeated database queries
Manages files in Supabase Storage buckets through MCP tools, supporting upload, download, list, and delete operations with automatic authentication and path-based access control. Uses Supabase's S3-compatible storage API with built-in support for public/private buckets and signed URLs for temporary access, enabling agents to handle file I/O without managing cloud storage credentials.
Unique: Exposes Supabase Storage's S3-compatible API as MCP tools with automatic authentication and signed URL generation, eliminating the need for agents to manage cloud storage credentials or generate temporary access tokens
vs alternatives: More integrated than generic S3 MCP tools because it leverages Supabase's built-in bucket policies and authentication rather than requiring separate AWS credentials
Performs semantic similarity searches on vector embeddings stored in Supabase PostgreSQL using pgvector extension, translating natural language queries into embedding vectors and executing cosine/L2 distance searches. Integrates with embedding providers (OpenAI, Cohere) or uses pre-computed embeddings, enabling agents to retrieve semantically similar documents or records without full-text search limitations.
Unique: Integrates pgvector directly into MCP tools with automatic embedding generation and distance calculation, enabling agents to perform semantic search without managing separate vector database infrastructure
vs alternatives: More efficient than external vector databases (Pinecone, Weaviate) for Supabase users because it colocates embeddings with relational data, reducing network latency and simplifying data synchronization
Exposes Supabase database schema information through MCP tools, allowing agents to discover table structures, column types, constraints, and relationships without manual schema documentation. Queries PostgreSQL information_schema and Supabase metadata tables to dynamically generate schema descriptions, enabling agents to construct valid queries and understand data relationships.
Unique: Queries Supabase's PostgreSQL information_schema directly through MCP tools, enabling agents to dynamically discover and adapt to database schemas without pre-configured schema definitions
vs alternatives: More flexible than static schema definitions because it reflects live database state, including recent migrations or schema changes
Enforces Supabase Row-Level Security policies within agent queries, ensuring that agents can only access rows permitted by RLS rules defined in the database. Evaluates policies based on authenticated user context (JWT claims, user ID) and applies WHERE clause filters automatically, preventing unauthorized data access at the database layer rather than application layer.
Unique: Delegates authorization enforcement to PostgreSQL RLS policies rather than implementing authorization in agent code, ensuring that data access rules are centralized and cannot be bypassed by agent logic
vs alternatives: More secure than application-level authorization because RLS is enforced at the database layer, preventing accidental data leaks even if agent code has bugs
+1 more capabilities