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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.","intents":["I want to connect Claude or another LLM to Apify Actors so it can trigger and manage web scraping tasks","I need to expose my Apify Actors as callable tools in an AI agent without building custom API wrappers","I want to standardize how my LLM interacts with Apify through the MCP protocol instead of direct HTTP calls"],"best_for":["AI agent developers building automation workflows that require web scraping or data extraction","Teams using Claude with MCP clients who want native Apify integration","Developers migrating from REST API calls to standardized MCP tool calling"],"limitations":["Requires running a separate Node.js process to host the MCP server — adds deployment complexity vs direct API calls","MCP protocol overhead adds ~50-100ms latency per tool invocation compared to direct HTTP","Limited to tools exposed by Apify's Actor API — cannot wrap custom business logic not available through Actors","No built-in authentication caching — each MCP request must include or refresh Apify API credentials"],"requires":["Node.js 16+","Apify API token (available from Apify account settings)","@modelcontextprotocol/sdk package","MCP-compatible client (Claude Desktop, custom MCP client, or compatible LLM interface)"],"input_types":["MCP tool call requests (JSON-RPC 2.0 format)","Actor configuration parameters (URLs, selectors, options)","Apify API credentials (token)"],"output_types":["MCP tool result responses (JSON-RPC 2.0)","Actor execution status and results","Structured data from Actor runs"],"categories":["tool-use-integration","mcp-server"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm_npm-apifyactors-mcp-server__cap_1","uri":"capability://tool.use.integration.actor.discovery.and.tool.schema.generation","name":"actor discovery and tool schema generation","description":"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.","intents":["I want my LLM to automatically discover which Actors I have available without manually defining tool schemas","I need the MCP server to generate accurate parameter schemas from my Actors' input definitions","I want to ensure the LLM understands what inputs each Actor accepts and what outputs it produces"],"best_for":["Teams with multiple Actors who want dynamic tool discovery without hardcoding schemas","Developers building multi-tenant systems where Actor availability varies per user","Organizations that frequently add or modify Actors and need schema updates without redeployment"],"limitations":["Schema generation depends on Actor developers providing complete input/output JSON schemas — incomplete schemas result in poor LLM understanding","Discovery latency scales with number of Actors in account — accounts with 100+ Actors may experience 2-5 second discovery delays","No caching of discovered schemas — each server restart triggers full re-discovery, adding startup time","Cannot introspect runtime Actor behavior — only static schema metadata is available"],"requires":["Apify API token with read access to Actor metadata","Actors must have properly defined input/output JSON schemas in their configuration","Network connectivity to Apify API (api.apify.com)"],"input_types":["Apify API token","Actor metadata from Apify API (JSON)"],"output_types":["MCP ToolDefinition objects","JSON schemas for tool parameters","Tool descriptions and usage hints"],"categories":["tool-use-integration","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm_npm-apifyactors-mcp-server__cap_2","uri":"capability://automation.workflow.actor.execution.and.result.streaming","name":"actor execution and result streaming","description":"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.","intents":["I want to trigger an Actor run from Claude and wait for results without polling manually","I need to pass parameters from an LLM tool call directly to an Actor's input","I want to handle Actor execution failures gracefully and return meaningful error messages to the LLM"],"best_for":["AI agents that need to execute long-running web scraping or data extraction tasks","Workflows where LLM decisions trigger Actor runs and subsequent steps depend on results","Teams building chatbots that perform real-time data collection via Actors"],"limitations":["Actor execution time is not bounded by MCP timeout — long-running Actors (>5 minutes) may cause MCP client timeouts","No built-in result streaming — entire Actor output is buffered and returned at completion, limiting real-time feedback","Cannot cancel in-flight Actor runs through MCP — cancellation requires separate Apify API calls","Result size is limited by MCP message size constraints — very large Actor outputs (>10MB) may fail to transmit","Polling-based status checks add latency — no webhook or event-driven execution model"],"requires":["Apify API token with Actor execution permissions","Actor must be published or in user's account","Sufficient Apify credits/quota for Actor execution","Network connectivity to Apify API"],"input_types":["MCP tool call with Actor name and input parameters","Actor input JSON matching Actor's input schema"],"output_types":["Actor execution status (queued, running, succeeded, failed)","Final Actor output (JSON or structured data)","Error messages and failure reasons"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm_npm-apifyactors-mcp-server__cap_3","uri":"capability://safety.moderation.parameter.validation.and.schema.enforcement","name":"parameter validation and schema enforcement","description":"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.","intents":["I want the MCP server to reject invalid parameters before sending them to Apify, saving execution time and credits","I need clear error messages when the LLM provides incorrect parameter types or missing required fields","I want to ensure the LLM understands parameter constraints (min/max values, allowed options, etc.)"],"best_for":["Production systems where invalid Actor runs waste credits and time","Teams using LLMs that occasionally hallucinate parameter values","Workflows requiring high reliability and minimal failed executions"],"limitations":["Validation only checks static schema constraints — cannot validate semantic correctness (e.g., valid URL format beyond regex)","Complex nested schemas with conditional requirements may not be fully validated","Error messages are technical JSON Schema validation errors — may not be user-friendly for non-technical LLM prompts","No custom validation hooks — cannot add business logic validation beyond JSON Schema"],"requires":["Actor input schema properly defined in Apify Actor configuration","JSON Schema validator library (typically ajv or similar)"],"input_types":["MCP tool call parameters (JSON)","Actor input JSON schema"],"output_types":["Validation success/failure status","Detailed validation error messages","Corrected parameter suggestions (optional)"],"categories":["safety-moderation","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm_npm-apifyactors-mcp-server__cap_4","uri":"capability://safety.moderation.apify.api.credential.management.and.authentication","name":"apify api credential management and authentication","description":"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.","intents":["I want to securely pass my Apify API token to the MCP server without exposing it to the LLM client","I need the MCP server to handle authentication failures and token expiration gracefully","I want to rotate API tokens without restarting the MCP server"],"best_for":["Production deployments where API credentials must be protected from client exposure","Teams with credential rotation policies requiring token refresh without downtime","Multi-user systems where different users have different Apify accounts"],"limitations":["No built-in credential encryption at rest — tokens are stored in memory only, lost on server restart","No support for OAuth or federated authentication — requires static API token","Cannot scope credentials per Actor — single token grants access to all user's Actors","No audit logging of credential usage — cannot track which clients used which credentials","Token rotation requires manual server configuration update — no automatic refresh mechanism"],"requires":["Apify API token (from Apify account settings)","Environment variable or configuration file to pass token to MCP server","Secure transport (HTTPS/TLS) if MCP server is exposed over network"],"input_types":["Apify API token (string)","Token validation requests"],"output_types":["Authentication status (valid/invalid)","Error messages for auth failures"],"categories":["safety-moderation","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm_npm-apifyactors-mcp-server__cap_5","uri":"capability://tool.use.integration.mcp.protocol.compliance.and.message.routing","name":"mcp protocol compliance and message routing","description":"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.","intents":["I want my MCP server to be compatible with Claude Desktop and other standard MCP clients","I need proper MCP error handling and protocol compliance for reliable client communication","I want the server to advertise available tools correctly so clients can discover and call them"],"best_for":["Developers building MCP servers that must work with multiple MCP clients","Teams using Claude Desktop or other MCP-compatible applications","Organizations standardizing on MCP for tool integration"],"limitations":["MCP protocol version is fixed at implementation time — cannot dynamically upgrade protocol without server restart","No built-in support for MCP extensions or custom protocol features — limited to standard MCP spec","Message size limits imposed by MCP protocol may truncate large Actor outputs","No streaming support in MCP — all results must be buffered and returned as complete messages","Protocol overhead adds latency compared to direct API calls"],"requires":["@modelcontextprotocol/sdk package","MCP-compatible client (Claude Desktop, custom MCP client)","Node.js 16+"],"input_types":["MCP JSON-RPC 2.0 requests","Tool call requests","Initialization messages"],"output_types":["MCP JSON-RPC 2.0 responses","Tool definitions","Tool results","Error responses"],"categories":["tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm_npm-apifyactors-mcp-server__cap_6","uri":"capability://automation.workflow.error.handling.and.failure.recovery","name":"error handling and failure recovery","description":"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.","intents":["I want the MCP server to retry failed Actor runs automatically instead of immediately failing","I need clear error messages when Apify APIs are unavailable or Actor execution fails","I want the server to handle network timeouts gracefully without hanging the MCP client"],"best_for":["Production systems requiring high availability and resilience","Workflows where transient failures should be retried automatically","Teams building mission-critical AI agents that cannot tolerate frequent failures"],"limitations":["Retry logic may mask underlying issues — repeated failures eventually exhaust retries without root cause analysis","Timeout values are fixed — cannot be dynamically adjusted per Actor or client","No circuit breaker pattern — server continues attempting Apify API calls even during extended outages","Error messages are generic MCP errors — may not include Apify-specific error details","No built-in alerting or monitoring — failures are only visible through MCP client logs"],"requires":["Network connectivity to Apify API","Proper timeout configuration in MCP server settings"],"input_types":["Failed API requests","Timeout events","Error responses from Apify"],"output_types":["MCP error responses","Retry status","Failure reasons and suggestions"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm_npm-apifyactors-mcp-server__cap_7","uri":"capability://automation.workflow.configuration.and.environment.setup","name":"configuration and environment setup","description":"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).","intents":["I want to configure the MCP server with environment variables for Docker/cloud deployment","I need to set custom logging levels and debug output for troubleshooting","I want to customize Actor discovery behavior (e.g., filter Actors by tag or category)"],"best_for":["DevOps teams deploying MCP servers in containerized environments","Developers setting up local development environments with minimal configuration","Organizations with complex deployment pipelines requiring environment-specific configs"],"limitations":["Configuration is static at server startup — runtime configuration changes require server restart","No built-in configuration validation — invalid settings may cause silent failures","Limited configuration options — cannot customize all MCP server behaviors","No configuration file schema or documentation — requires reading source code to understand options","Environment variable naming conventions may conflict with other tools"],"requires":["Node.js environment with access to environment variables","Configuration file (optional, format depends on implementation)"],"input_types":["Environment variables","Configuration files (JSON, YAML, or similar)","Programmatic configuration objects"],"output_types":["Validated configuration object","Configuration validation errors"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":37,"verified":false,"data_access_risk":"high","permissions":["Node.js 16+","Apify API token (available from Apify account settings)","@modelcontextprotocol/sdk package","MCP-compatible client (Claude Desktop, custom MCP client, or compatible LLM interface)","Apify API token with read access to Actor metadata","Actors must have properly defined input/output JSON schemas in their configuration","Network connectivity to Apify API (api.apify.com)","Apify API token with Actor execution permissions","Actor must be published or in user's account","Sufficient Apify credits/quota for Actor execution"],"failure_modes":["Requires running a separate Node.js process to host the MCP server — adds deployment complexity vs direct API calls","MCP protocol overhead adds ~50-100ms latency per tool invocation compared to direct HTTP","Limited to tools exposed by Apify's Actor API — cannot wrap custom business logic not available through Actors","No built-in authentication caching — each MCP request must include or refresh Apify API credentials","Schema generation depends on Actor developers providing complete input/output JSON schemas — incomplete schemas result in poor LLM understanding","Discovery latency scales with number of Actors in account — accounts with 100+ Actors may experience 2-5 second discovery delays","No caching of discovered schemas — each server restart triggers full re-discovery, adding startup time","Cannot introspect runtime Actor behavior — only static schema metadata is available","Actor execution time is not bounded by MCP timeout — long-running Actors (>5 minutes) may cause MCP client timeouts","No built-in result streaming — entire Actor output is buffered and returned at completion, limiting real-time feedback","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.4699952998848945,"quality":0.26,"ecosystem":0.45,"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":"2026-05-24T12:16:23.903Z","last_scraped_at":"2026-05-03T14:23:31.500Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":11193,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=npm-apifyactors-mcp-server","compare_url":"https://unfragile.ai/compare?artifact=npm-apifyactors-mcp-server"}},"signature":"FgT510yiN/z9ExJM82YlTMcjF3SLyIBLYDVvSp4xFCUgZ/+/bFcudTEmxR3M6+13iTyOvRs4w93ar8aEQO1+AQ==","signedAt":"2026-06-22T15:43:18.109Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/npm-apifyactors-mcp-server","artifact":"https://unfragile.ai/npm-apifyactors-mcp-server","verify":"https://unfragile.ai/api/v1/verify?slug=npm-apifyactors-mcp-server","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"}}