{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"npm-apify-actors-mcp-server","slug":"apify-actors-mcp-server","name":"@apify/actors-mcp-server","type":"mcp","url":"https://mcp.apify.com","page_url":"https://unfragile.ai/apify-actors-mcp-server","categories":["mcp-servers"],"tags":["apify","mcp","server","actors","model context protocol"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"npm-apify-actors-mcp-server__cap_0","uri":"capability://tool.use.integration.actor.execution.via.mcp.protocol","name":"actor execution via mcp protocol","description":"Exposes Apify Actors as MCP tools that Claude and other MCP clients can invoke directly. Implements the Model Context Protocol specification to translate tool-call requests into Apify Actor API calls, handling authentication, payload marshaling, and result streaming back to the client. Uses MCP's standardized tool schema to describe Actor inputs and outputs, enabling seamless integration with LLM-based agents without custom integration code.","intents":["I want Claude to trigger web scraping or data processing tasks on Apify without leaving the conversation","I need to build an AI agent that can invoke my existing Apify Actors as part of a multi-step workflow","I want to expose my Apify infrastructure to Claude's native tool-calling system"],"best_for":["AI agent builders using Claude or other MCP-compatible LLMs","Teams with existing Apify Actors who want to integrate them into LLM workflows","Developers building automation pipelines that combine web scraping with AI reasoning"],"limitations":["Requires Apify account and valid API token — cannot work offline or with self-hosted Actor infrastructure","Actor execution latency (typically 10-60 seconds) means synchronous tool calls may timeout in some LLM contexts","Limited to Actors already deployed on Apify platform — cannot invoke local or custom-built Actors directly","MCP protocol overhead adds ~500ms per tool invocation for serialization and network round-trip"],"requires":["Apify account with API token","MCP-compatible client (Claude Desktop, or custom MCP host)","Node.js 16+ for running the MCP server","Network access to api.apify.com"],"input_types":["structured JSON matching Actor input schema","string parameters for simple Actors","file references for batch processing Actors"],"output_types":["structured JSON from Actor results","streamed text output from long-running Actors","dataset references for large result sets"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-apify-actors-mcp-server__cap_1","uri":"capability://tool.use.integration.actor.discovery.and.schema.introspection","name":"actor discovery and schema introspection","description":"Automatically discovers all Actors available in an Apify account and generates MCP-compliant tool schemas describing their inputs, outputs, and execution parameters. Introspects Actor metadata (name, description, input schema, expected output format) from Apify's API and transforms it into MCP ToolDefinition objects that LLM clients can parse and present to users. Caches schema information to avoid repeated API calls during agent planning phases.","intents":["I want Claude to know what Actors I have available without manually describing them","I need to dynamically expose new Actors to my MCP client without code changes","I want the LLM to understand Actor input requirements and constraints before attempting execution"],"best_for":["Teams with large Actor portfolios (10+ Actors) who want dynamic tool discovery","Developers building multi-tenant MCP servers where Actor availability changes per user","Organizations migrating existing Apify workflows to LLM-based automation"],"limitations":["Schema introspection only works for Actors with properly documented input schemas — legacy Actors with missing metadata will appear with incomplete descriptions","Cache invalidation is time-based (default 1 hour) — newly deployed Actors may not appear immediately in MCP client","Complex nested input schemas with conditional fields may not translate perfectly to MCP's flat tool parameter model","No support for Actor versioning — always exposes the latest version regardless of client preference"],"requires":["Apify API token with read permissions on Actor metadata","Actors deployed on Apify platform with input schema defined","MCP client that supports dynamic tool discovery"],"input_types":["Apify account context (implicit from API token)"],"output_types":["MCP ToolDefinition objects with schema","JSON schema describing Actor inputs","Markdown descriptions of Actor capabilities"],"categories":["tool-use-integration","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-apify-actors-mcp-server__cap_10","uri":"capability://automation.workflow.actor.execution.with.request.context.and.metadata.propagation","name":"actor execution with request context and metadata propagation","description":"Propagates execution context (user ID, session ID, request ID, custom metadata) through Actor invocations, enabling traceability and correlation across distributed executions. Injects context into Actor environment variables and logs, allowing Actors to include context in their output for audit trails. Supports custom metadata tags that agents can attach to Actor runs for filtering and analysis.","intents":["I want to trace Actor executions back to the originating user or session","I need to correlate Actor runs across multiple invocations for debugging","I want to tag Actor runs with custom metadata for later filtering and analysis"],"best_for":["Multi-tenant systems where execution traceability is required","Debugging scenarios where correlating logs across Actors is critical","Compliance workflows where audit trails must be maintained"],"limitations":["Context propagation requires Actor code to read and use environment variables — legacy Actors may not support this","Metadata is stored in Apify but not indexed — filtering large result sets by metadata requires post-processing","Context size is limited (typically <1KB) — cannot propagate large payloads","Custom metadata is not validated — agents must ensure metadata is well-formed"],"requires":["Actor code that reads context from environment variables (optional)","Apify API support for custom metadata (included in recent versions)"],"input_types":["context object (user_id, session_id, request_id, custom_metadata)"],"output_types":["Actor run with context embedded in environment","logs and output tagged with context metadata"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-apify-actors-mcp-server__cap_11","uri":"capability://automation.workflow.actor.execution.with.rate.limiting.and.concurrency.control","name":"actor execution with rate limiting and concurrency control","description":"Enforces rate limits on Actor invocations to prevent overwhelming Apify infrastructure or exceeding account concurrency limits. Implements token-bucket rate limiting with configurable rates (e.g., max 10 concurrent Actors, max 100 invocations per minute). Queues excess invocations and executes them as capacity becomes available, providing agents with visibility into queue status and estimated wait times.","intents":["I want to prevent my agent from overwhelming Apify with too many concurrent Actors","I need to respect my account's concurrency limits without manual coordination","I want to know how long an Actor will wait in the queue before execution"],"best_for":["High-throughput agents that invoke many Actors in rapid succession","Multi-tenant MCP servers where rate limiting must be enforced per user","Workflows with strict resource constraints that cannot tolerate concurrent execution"],"limitations":["Rate limiting adds latency — queued Actors may wait minutes before execution","Queue state is not persisted — if MCP server restarts, queued invocations are lost","Rate limits are per-server instance — distributed MCP servers cannot coordinate limits","Agents have no control over queue priority — all queued invocations are FIFO"],"requires":["Rate limit configuration (max concurrent, max per minute)","Apify account with known concurrency limits"],"input_types":["rate limit configuration"],"output_types":["execution status (immediate, queued, or rejected)","queue position and estimated wait time","rate limit metrics (current concurrency, invocations this minute)"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-apify-actors-mcp-server__cap_2","uri":"capability://tool.use.integration.actor.result.streaming.and.pagination","name":"actor result streaming and pagination","description":"Streams Actor execution results back to the MCP client in real-time, handling pagination for large datasets and chunking output into manageable pieces. Implements streaming via MCP's text content blocks, allowing long-running Actors to return partial results as they complete. Automatically handles Apify's dataset pagination API, fetching results in batches and presenting them to the client without requiring manual offset/limit management.","intents":["I want to see Actor results as they complete rather than waiting for full execution","I need to process large result sets (100k+ items) without loading everything into memory at once","I want Claude to reason over partial results while an Actor is still running"],"best_for":["Long-running web scraping tasks where intermediate results are valuable","Batch processing workflows where results are consumed incrementally","Interactive agent sessions where users want real-time feedback on Actor progress"],"limitations":["Streaming only works for Actors that output to Apify datasets — Actors returning direct JSON responses are buffered entirely before returning","Large result sets (>10MB) may still cause memory pressure in the MCP client even with pagination","No built-in deduplication or filtering — client receives all results including duplicates if Actor produces them","Streaming adds latency for small result sets (<1MB) due to chunking overhead"],"requires":["Actor configured to output results to Apify dataset","MCP client supporting streaming content blocks","Sufficient network bandwidth for continuous result streaming"],"input_types":["Actor execution context (implicit)"],"output_types":["streamed text chunks via MCP TextContent blocks","structured JSON for each result batch","pagination metadata (offset, limit, total count)"],"categories":["tool-use-integration","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-apify-actors-mcp-server__cap_3","uri":"capability://tool.use.integration.actor.execution.monitoring.and.status.tracking","name":"actor execution monitoring and status tracking","description":"Tracks Actor execution state (running, succeeded, failed, timed out) and exposes status information to the MCP client via tool results and optional status callbacks. Polls Apify's Actor run API at configurable intervals to detect completion, failures, and resource constraints. Provides structured error messages including failure reasons, logs, and resource usage metrics that help LLM agents understand why an Actor failed and decide whether to retry or escalate.","intents":["I want Claude to know when an Actor finishes and what the result was","I need to handle Actor failures gracefully in my agent workflow","I want visibility into why an Actor failed (timeout, resource limit, input validation error)"],"best_for":["Agents that need to make decisions based on Actor success/failure","Workflows with fallback logic (retry with different parameters, use alternative Actor)","Debugging scenarios where understanding failure reasons is critical"],"limitations":["Status polling adds latency — default 5-second poll interval means up to 5 seconds before client learns of completion","No webhook support — must use polling, which scales poorly with many concurrent Actor runs","Timeout handling is client-side — if MCP client disconnects, monitoring stops even if Actor continues running","Error messages are limited to Apify's provided logs — cannot capture Actor stdout/stderr directly"],"requires":["Apify API token with read permissions on Actor runs","Network connectivity to Apify API during Actor execution","MCP client that can wait for asynchronous tool results"],"input_types":["Actor run ID (returned from execution)"],"output_types":["structured status object (state, progress, resource usage)","error details with failure reason and logs","execution metrics (duration, memory used, API calls made)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-apify-actors-mcp-server__cap_4","uri":"capability://tool.use.integration.actor.input.validation.and.schema.enforcement","name":"actor input validation and schema enforcement","description":"Validates Actor input parameters against the Actor's declared input schema before execution, catching configuration errors early and providing detailed validation error messages. Uses JSON schema validation to check required fields, type constraints, and value ranges. Returns validation errors to the LLM client before attempting execution, allowing agents to correct inputs or request user clarification rather than wasting Actor execution time on invalid inputs.","intents":["I want Claude to validate Actor inputs before triggering execution","I need clear error messages when Actor parameters are invalid or missing","I want to prevent wasted Actor runs due to configuration mistakes"],"best_for":["Agents that need to construct complex Actor inputs dynamically","Workflows where Actor execution cost is high and validation prevents waste","Multi-step processes where early validation prevents cascading failures"],"limitations":["Validation only checks schema constraints — cannot validate semantic correctness (e.g., whether a URL is actually reachable)","Complex conditional schemas (if field A is set, then field B is required) may not be fully validated","Custom validation rules defined in Actor code are not enforced by MCP server","Validation adds ~50-100ms latency per tool call"],"requires":["Actor with properly defined input schema in Apify","JSON schema validation library (included in MCP server)"],"input_types":["JSON object matching Actor input schema"],"output_types":["validation success confirmation","detailed validation error messages with field paths and constraints"],"categories":["tool-use-integration","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-apify-actors-mcp-server__cap_5","uri":"capability://automation.workflow.multi.actor.orchestration.and.chaining","name":"multi-actor orchestration and chaining","description":"Enables sequential or parallel execution of multiple Actors within a single agent workflow, with output from one Actor automatically passed as input to the next. Implements dependency tracking to ensure Actors execute in the correct order, and provides utilities for transforming output from one Actor into the input format expected by the next. Handles error propagation — if an Actor in a chain fails, subsequent Actors are skipped unless the agent explicitly implements retry logic.","intents":["I want to scrape data with one Actor, then process it with another Actor in sequence","I need to run multiple Actors in parallel and combine their results","I want to build complex data pipelines where each Actor handles one step"],"best_for":["Multi-step data processing workflows (scrape → transform → load)","Agents that need to compose Actors to achieve complex goals","Teams building reusable Actor chains that are invoked repeatedly"],"limitations":["No built-in state persistence — if the MCP client disconnects mid-chain, progress is lost","Parallel execution is limited by Apify account concurrency limits — cannot guarantee all Actors start simultaneously","Output transformation between Actors must be handled by the agent or via custom mapping logic","No rollback capability — if a later Actor fails, earlier results remain in Apify datasets"],"requires":["Multiple Actors deployed on Apify with compatible input/output schemas","MCP client capable of managing multi-step workflows","Sufficient Apify account concurrency limits for parallel execution"],"input_types":["initial input for first Actor in chain"],"output_types":["final output from last Actor in chain","intermediate results from each step (if requested)"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-apify-actors-mcp-server__cap_6","uri":"capability://tool.use.integration.actor.execution.with.custom.environment.and.memory.configuration","name":"actor execution with custom environment and memory configuration","description":"Allows specifying Actor execution parameters including memory allocation, timeout duration, and build version at invocation time. Translates these parameters into Apify Actor run API options, enabling agents to optimize resource usage based on task requirements. Supports memory scaling (256MB to 32GB) and timeout configuration (30 seconds to 24 hours) to handle both lightweight and resource-intensive workloads without requiring separate Actor versions.","intents":["I want to run a memory-intensive Actor with 16GB instead of the default 8GB","I need to set a custom timeout for a long-running scraping task","I want to test an Actor against a specific build version before deploying to production"],"best_for":["Agents that need to dynamically adjust resource allocation based on task complexity","Workflows with variable performance requirements (some runs need more memory than others)","Testing and debugging scenarios where specific build versions need to be invoked"],"limitations":["Higher memory allocation increases Actor execution cost — agents must balance resource usage with budget constraints","Timeout configuration is advisory — Apify may forcefully terminate Actors exceeding platform limits regardless of requested timeout","Build version selection requires knowledge of available builds — no validation that specified build exists","Memory scaling is quantized (256MB increments) — cannot request arbitrary memory amounts"],"requires":["Apify account with sufficient quota for requested memory tier","Knowledge of available Actor build versions"],"input_types":["memory allocation (256-32768 MB)","timeout duration (30-86400 seconds)","build version identifier"],"output_types":["execution confirmation with allocated resources","resource usage metrics from completed run"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-apify-actors-mcp-server__cap_7","uri":"capability://data.processing.analysis.actor.output.format.transformation.and.normalization","name":"actor output format transformation and normalization","description":"Automatically transforms Actor output into standardized formats (JSON, CSV, markdown) suitable for LLM consumption. Handles conversion from Apify's native dataset format into structured JSON, flattens nested objects for readability, and optionally summarizes large result sets into representative samples. Supports custom transformation rules to map Actor-specific output fields into domain-specific schemas that agents understand.","intents":["I want Actor results formatted as clean JSON that Claude can easily parse","I need to convert scraped data into a standard format for downstream processing","I want to summarize large result sets into a representative sample for the LLM to analyze"],"best_for":["Agents that need to reason over Actor results in standardized formats","Workflows where Actor output schemas vary and need normalization","Scenarios where large result sets must be summarized for LLM context windows"],"limitations":["Custom transformation rules require manual configuration — no automatic schema mapping","Summarization via sampling may miss important outliers or edge cases in large datasets","Flattening nested objects can lose structural information (e.g., hierarchical relationships)","Format conversion adds ~100-500ms latency depending on result set size"],"requires":["Actor output in Apify dataset format","Transformation rules defined in MCP server configuration (optional)"],"input_types":["raw Actor output from Apify dataset"],"output_types":["JSON (structured, flattened, or nested)","CSV (tabular format)","markdown (human-readable summary)","sampled dataset (representative subset of large results)"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-apify-actors-mcp-server__cap_8","uri":"capability://automation.workflow.actor.cost.estimation.and.budget.tracking","name":"actor cost estimation and budget tracking","description":"Estimates Actor execution cost before invocation based on memory allocation, timeout, and historical execution patterns. Tracks cumulative costs across multiple Actor runs within a session and provides budget alerts when approaching spending limits. Integrates with Apify's pricing API to fetch current rates and calculate costs in the agent's configured currency. Allows agents to make cost-aware decisions (e.g., skip expensive Actors if budget is depleted).","intents":["I want to know how much an Actor will cost before running it","I need to track total spending across multiple Actor invocations","I want to prevent runaway costs by setting budget limits"],"best_for":["Cost-sensitive workflows where budget constraints are critical","Multi-tenant MCP servers where per-user cost tracking is required","Agents that need to make trade-off decisions between accuracy and cost"],"limitations":["Cost estimates are based on historical data and may not reflect actual costs for novel workloads","Pricing API calls add ~100-200ms latency per cost estimation","Budget tracking is session-scoped — no persistence across client reconnections","Currency conversion is approximate and may lag real exchange rates"],"requires":["Apify API token with billing read permissions","Historical execution data to train cost models"],"input_types":["Actor ID, memory allocation, timeout"],"output_types":["estimated cost in configured currency","cost breakdown by resource type (compute, storage, API calls)","cumulative session cost","budget remaining"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-apify-actors-mcp-server__cap_9","uri":"capability://automation.workflow.actor.execution.with.retry.and.fallback.logic","name":"actor execution with retry and fallback logic","description":"Implements automatic retry mechanisms for failed Actor executions with configurable backoff strategies (exponential, linear, fixed). Supports fallback Actors — if the primary Actor fails, automatically invoke an alternative Actor with compatible output schema. Tracks retry attempts and provides detailed failure history to help agents understand failure patterns and decide whether to continue retrying or escalate to user.","intents":["I want failed Actors to automatically retry with exponential backoff","I need to fall back to an alternative Actor if the primary one fails","I want visibility into why an Actor failed after multiple retry attempts"],"best_for":["Workflows with transient failures (network timeouts, rate limits) that benefit from retries","Agents that need resilience against individual Actor failures","Multi-Actor pipelines where fallback Actors provide alternative implementations"],"limitations":["Retry logic increases total execution time — exponential backoff can add minutes to failed workflows","Fallback Actors must have compatible output schemas — no automatic schema adaptation","Retry exhaustion may still result in failure — retries don't fix systematic issues (invalid input, Actor bugs)","Retry state is not persisted — if MCP client disconnects, retry tracking is lost"],"requires":["Retry configuration (max attempts, backoff strategy)","Fallback Actor IDs (optional)"],"input_types":["retry configuration (max attempts: 1-10, backoff: exponential|linear|fixed)"],"output_types":["final result after successful retry","failure details after retry exhaustion","retry history with timestamps and error reasons"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":40,"verified":false,"data_access_risk":"moderate","permissions":["Apify account with API token","MCP-compatible client (Claude Desktop, or custom MCP host)","Node.js 16+ for running the MCP server","Network access to api.apify.com","Apify API token with read permissions on Actor metadata","Actors deployed on Apify platform with input schema defined","MCP client that supports dynamic tool discovery","Actor code that reads context from environment variables (optional)","Apify API support for custom metadata (included in recent versions)","Rate limit configuration (max concurrent, max per minute)"],"failure_modes":["Requires Apify account and valid API token — cannot work offline or with self-hosted Actor infrastructure","Actor execution latency (typically 10-60 seconds) means synchronous tool calls may timeout in some LLM contexts","Limited to Actors already deployed on Apify platform — cannot invoke local or custom-built Actors directly","MCP protocol overhead adds ~500ms per tool invocation for serialization and network round-trip","Schema introspection only works for Actors with properly documented input schemas — legacy Actors with missing metadata will appear with incomplete descriptions","Cache invalidation is time-based (default 1 hour) — newly deployed Actors may not appear immediately in MCP client","Complex nested input schemas with conditional fields may not translate perfectly to MCP's flat tool parameter model","No support for Actor versioning — always exposes the latest version regardless of client preference","Context propagation requires Actor code to read and use environment variables — legacy Actors may not support this","Metadata is stored in Apify but not indexed — filtering large result sets by metadata requires post-processing","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.4699952998848945,"quality":0.34,"ecosystem":0.55,"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":"2026-05-24T12:16:23.328Z","last_scraped_at":"2026-05-03T14:04:47.471Z","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=apify-actors-mcp-server","compare_url":"https://unfragile.ai/compare?artifact=apify-actors-mcp-server"}},"signature":"MPMGuARUFXl8Ulk5yJ/G0L0Ao1qI0e58L0DFar1mY9hERubQu2QYqJ71stYhi8JIhpLcRfEGd20TXBJewMh2Aw==","signedAt":"2026-06-22T22:26:07.489Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/apify-actors-mcp-server","artifact":"https://unfragile.ai/apify-actors-mcp-server","verify":"https://unfragile.ai/api/v1/verify?slug=apify-actors-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"}}