| 0 |
| Ecosystem | 1 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 12 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Exposes thousands of Apify Actors as standardized MCP tools through the ActorsMcpServer class, which registers tools with structured JSON schemas and handles MCP protocol operations (tool discovery, invocation, result streaming). The server implements the Model Context Protocol specification, enabling AI clients (Claude Desktop, VS Code, ChatGPT) to discover and invoke Actors as first-class tools with type-safe input/output contracts.
Unique: Implements full MCP server specification with three tool types (actor, internal, actor-mcp) and dynamic schema transformation from Apify Actor definitions, enabling seamless integration of 1000+ pre-built scrapers without custom wrapper code. Uses ActorsMcpServer class to manage tool registration, session state, and telemetry collection.
vs alternatives: Provides standardized MCP interface to Apify's ecosystem whereas custom REST API wrappers require manual schema definition and client-side tool discovery logic
Supports three transport protocols for MCP communication: STDIO for local CLI usage (Claude Desktop integration), SSE for legacy streaming, and HTTP for hosted services. The transport layer abstracts protocol differences, allowing the same ActorsMcpServer core to operate across deployment contexts (local, Apify Actor standby mode, or hosted service at mcp.apify.com) without code changes.
Unique: Abstracts transport protocol differences through a unified server interface, enabling deployment across three distinct contexts (local CLI, serverless Actor, hosted service) from the same codebase. STDIO transport directly integrates with Claude Desktop via stdio.ts without requiring network overhead.
vs alternatives: Eliminates need for separate server implementations per transport protocol; competitors typically require distinct codebases or configuration layers for local vs. hosted deployment
Provides built-in internal helper tools such as 'fetch-apify-docs' that enable agents to access Apify documentation, platform guides, and best practices without external API calls. These tools are implemented as internal type tools within the MCP server, allowing agents to self-serve documentation lookups and troubleshoot issues autonomously.
Unique: Exposes Apify documentation as internal MCP tools, enabling agents to autonomously access guides and troubleshooting information without external API calls. Reduces agent context window usage by providing targeted documentation lookups.
vs alternatives: Provides built-in documentation access versus requiring agents to search external documentation; reduces context window overhead and improves agent autonomy
Manages session state across multiple MCP tool invocations, enabling multi-turn workflows where agents maintain context about previous operations, selected Actors, and execution history. The server tracks session metadata, task history, and user preferences, allowing agents to reference prior decisions and results without re-querying or re-executing.
Unique: Implements session management within the MCP server to track state across multi-turn workflows, enabling agents to maintain context about prior operations without re-querying or re-executing. Stores execution history and user preferences per session.
vs alternatives: Provides built-in session state management versus requiring clients to implement context tracking; simplifies multi-turn agent workflows
Provides a built-in 'search-actors' internal tool that queries the Apify Store to discover Actors matching user intent, with semantic filtering based on descriptions, tags, and categories. The tool integrates with the Apify API to retrieve Actor metadata, schemas, and pricing information, enabling AI agents to autonomously select appropriate scrapers/crawlers for data extraction tasks without manual tool selection.
Unique: Implements semantic Actor discovery as a first-class MCP tool, allowing AI agents to autonomously search and select from 1000+ Actors based on natural language intent rather than requiring manual tool selection. Integrates directly with Apify Store API for real-time metadata.
vs alternatives: Enables agents to discover tools dynamically versus static tool lists; competitors require manual curation or external search systems
Manages asynchronous execution of long-running Actors through a task storage system that tracks in-flight operations, polls for completion status, and retrieves results without blocking the MCP client. The server maintains a task registry (likely in-memory or persistent storage) that maps task IDs to Actor run metadata, enabling clients to check status and fetch results via separate MCP tool calls rather than waiting for synchronous completion.
Unique: Implements task storage and polling within the MCP server itself, allowing clients to manage long-running operations through standard MCP tool calls without custom async handling. Decouples execution from result retrieval, enabling agents to parallelize multiple Actor runs.
vs alternatives: Provides built-in async task management versus requiring clients to implement custom polling logic or use webhooks; simplifies agent orchestration of multi-step workflows
Transforms Apify Actor input schemas into MCP-compliant tool schemas through schema processing logic that handles type mapping, constraint validation, and widget generation. The server parses Actor JSON schemas, applies transformations to match MCP expectations, and generates UI widgets (for OpenAI mode) that guide users through complex input parameters. This enables type-safe invocation of Actors with heterogeneous input requirements.
Unique: Implements bidirectional schema transformation from Apify Actor definitions to MCP schemas with widget generation for OpenAI mode, enabling type-safe tool invocation without manual schema definition. Uses schema processing logic to map Actor constraints to MCP validation rules.
vs alternatives: Automates schema adaptation versus manual MCP schema definition; provides widget generation for UI-based tool configuration that competitors lack
Enables the Apify MCP server to proxy tools from other MCP servers that have been 'Actorized' (wrapped as Apify Actors), exposing them as actor-mcp type tools. This creates a composable MCP ecosystem where tools from external MCP servers can be discovered and invoked through the Apify server without direct client-to-server connections, enabling tool chaining and multi-server orchestration.
Unique: Implements actor-mcp tool type to proxy external MCP server tools through Apify Actors, creating a composable MCP ecosystem where tools from multiple servers can be orchestrated through a single MCP client connection. Enables tool chaining without direct multi-server management.
vs alternatives: Simplifies multi-server tool orchestration versus requiring clients to manage separate MCP connections; enables tool composition through a single hub
+4 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 apify-mcp-server at 35/100.
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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