| 0 |
| Ecosystem | 0 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 8 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Bootstraps a Model Context Protocol server that exposes Apify Actor APIs as MCP tools, implementing the MCP server specification to translate HTTP-based Actor endpoints into standardized tool schemas. Uses the @modelcontextprotocol/sdk to handle MCP protocol negotiation, tool registration, and bidirectional message routing between MCP clients (Claude, other LLMs) and Apify's Actor execution platform.
Unique: Implements MCP server specification specifically for Apify's Actor platform, translating Actor HTTP APIs into standardized MCP tool schemas with automatic schema generation from Actor input/output definitions
vs alternatives: Provides native MCP integration for Apify Actors without custom wrapper code, whereas direct HTTP calls require manual schema definition and lack MCP protocol standardization
Automatically discovers available Apify Actors in a user's account and generates MCP-compliant tool schemas by introspecting Actor input specifications and output formats. Queries the Apify API to fetch Actor metadata, parses input/output JSON schemas, and converts them into MCP ToolDefinition objects with proper parameter typing, descriptions, and validation rules.
Unique: Performs dynamic schema generation by parsing Apify Actor input/output definitions and converting them to MCP ToolDefinition format, enabling zero-configuration tool exposure without manual schema authoring
vs alternatives: Eliminates manual schema definition compared to generic MCP servers, automatically staying in sync with Actor configuration changes
Executes Apify Actors through the MCP protocol by translating tool calls into Actor run requests, managing the execution lifecycle (queuing, running, completion), and streaming results back to the MCP client. Handles asynchronous Actor execution by polling the Apify API for run status, buffering intermediate results, and returning final outputs in MCP-compatible format with error handling and timeout management.
Unique: Manages full Actor execution lifecycle through MCP protocol, handling asynchronous polling, result buffering, and timeout/error recovery without requiring the LLM client to manage execution state
vs alternatives: Abstracts Actor execution complexity compared to direct API calls, providing synchronous-style tool calling interface for asynchronous Actor runs
Validates MCP tool call parameters against Actor input schemas before execution, enforcing type constraints, required fields, and allowed values defined in the Actor's JSON schema. Implements JSON Schema validation using standard validators, rejecting invalid parameters with detailed error messages that guide the LLM to correct inputs, preventing failed Actor runs due to malformed inputs.
Unique: Performs pre-execution JSON Schema validation against Actor input definitions, preventing invalid tool calls from reaching Apify and providing schema-aware error feedback to LLM clients
vs alternatives: Catches parameter errors before API calls compared to post-execution error handling, reducing wasted credits and improving LLM feedback loops
Manages Apify API authentication by accepting and securely handling API tokens, implementing credential validation, and injecting authentication headers into all Apify API requests. Supports token rotation, credential refresh, and error handling for expired/invalid tokens, ensuring the MCP server maintains authenticated access to Apify APIs without exposing credentials to MCP clients.
Unique: Centralizes Apify API authentication at the MCP server level, preventing credentials from being transmitted to or stored by MCP clients while maintaining secure API access
vs alternatives: Isolates credential handling from LLM clients compared to client-side authentication, reducing credential exposure surface area
Implements the Model Context Protocol specification, handling JSON-RPC 2.0 message parsing, tool definition advertisement, and request/response routing between MCP clients and Apify APIs. Manages MCP lifecycle events (initialization, tool listing, tool execution), error handling with proper MCP error codes, and protocol versioning to ensure compatibility with MCP-compliant clients like Claude Desktop.
Unique: Implements full MCP server specification with JSON-RPC 2.0 message handling, tool advertisement, and lifecycle management, ensuring seamless integration with MCP-compliant clients
vs alternatives: Provides standards-based protocol implementation compared to custom API wrappers, enabling compatibility with any MCP client
Implements comprehensive error handling for Apify API failures, network issues, timeouts, and invalid Actor configurations, translating errors into MCP-compatible error responses with actionable messages. Includes retry logic for transient failures, timeout management for long-running Actors, and graceful degradation when Apify APIs are unavailable, ensuring the MCP server remains stable and provides meaningful feedback to clients.
Unique: Implements MCP-aware error handling with retry logic and timeout management, translating Apify API errors into standardized MCP error responses with recovery suggestions
vs alternatives: Provides automatic retry and timeout handling compared to client-side error management, improving reliability without requiring client-side retry logic
Manages MCP server configuration through environment variables, configuration files, or programmatic setup, including Apify API token, server port, logging level, and Actor discovery settings. Provides initialization hooks for custom configuration loading, validation of required settings, and defaults for optional parameters, enabling flexible deployment across different environments (local development, Docker, cloud platforms).
Unique: Provides flexible configuration management through environment variables and configuration files, supporting multiple deployment scenarios without code changes
vs alternatives: Enables environment-specific configuration compared to hardcoded settings, supporting diverse deployment contexts
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 @apify/actors-mcp-server at 34/100. @apify/actors-mcp-server leads on adoption and ecosystem, while Supabase is stronger on quality.
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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
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