{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github_mcp-activepieces-activepieces","slug":"mcp-activepieces-activepieces","name":"activepieces","type":"mcp","url":"https://github.com/activepieces/activepieces","page_url":"https://unfragile.ai/mcp-activepieces-activepieces","categories":["mcp-servers","automation"],"tags":["ai-agent","ai-agent-tools","ai-agents","ai-agents-framework","mcp","mcp-server","mcp-tools","mcps","n8n-alternative","no-code-automation","workflow","workflow-automation","workflows"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github_mcp-activepieces-activepieces__cap_0","uri":"capability://automation.workflow.declarative.flow.definition.and.visual.workflow.composition","name":"declarative flow definition and visual workflow composition","description":"Activepieces enables users to define automation workflows declaratively through a visual flow builder UI that compiles to an intermediate representation executed by the flow execution engine. The system uses a directed acyclic graph (DAG) model where flows consist of triggers, actions, routers, and loops connected via data bindings. The frontend state management captures the flow structure and persists it to the backend database, while the engine deserializes and executes the flow step-by-step with full context propagation between steps.","intents":["I want to build multi-step automation workflows without writing code","I need to visually connect API calls, data transformations, and conditional logic","I want to reuse workflow templates and share them with my team"],"best_for":["non-technical business users automating repetitive tasks","teams migrating from Zapier or n8n seeking open-source alternative","enterprises needing self-hosted workflow automation with audit trails"],"limitations":["Flow complexity is limited by DAG structure — no arbitrary control flow patterns","Visual builder performance degrades with flows exceeding 50+ steps due to frontend rendering","No built-in version control for flows — requires external Git sync integration"],"requires":["Node.js 18+ for backend server","PostgreSQL 12+ for flow persistence and execution history","Modern browser with ES2020+ support for flow builder UI"],"input_types":["JSON configuration objects","Webhook payloads","Trigger event data"],"output_types":["Flow execution results","Step output data","Execution logs and run history"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-activepieces-activepieces__cap_1","uri":"capability://automation.workflow.trigger.based.event.driven.workflow.activation","name":"trigger-based event-driven workflow activation","description":"Activepieces supports multiple trigger types (webhooks, polling, AI agent invocations, scheduled cron) that activate flows when external events occur. Triggers are implemented as pieces with special lifecycle hooks that register listeners or polling intervals. The system maintains trigger state (last poll time, webhook subscriptions) in the database and uses a queue-based worker architecture to dequeue trigger events and spawn flow executions. Webhook triggers expose unique URLs per flow instance, while polling triggers run on configurable intervals via the worker pool.","intents":["I want flows to automatically start when a webhook receives data from an external service","I need scheduled workflows that run at specific times or intervals","I want AI agents to trigger workflows by invoking them as tools"],"best_for":["event-driven automation scenarios (Slack messages, GitHub webhooks, form submissions)","scheduled batch processing and data synchronization tasks","AI agent orchestration where agents invoke workflows as external tools"],"limitations":["Polling triggers have minimum 1-minute interval due to worker scheduling overhead","Webhook triggers require public URL exposure or reverse proxy setup for self-hosted instances","No built-in deduplication — duplicate webhook payloads may trigger multiple executions"],"requires":["Webhook triggers: publicly accessible API endpoint or ngrok tunnel","Polling triggers: worker process running (packages/server/worker)","Cron triggers: system timezone configuration in deployment environment"],"input_types":["HTTP POST/GET requests","Cron schedule expressions","Polling interval configuration"],"output_types":["Flow execution ID","Trigger event payload","Execution status (queued, running, completed)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-activepieces-activepieces__cap_10","uri":"capability://automation.workflow.loop.iteration.over.arrays.with.step.repetition","name":"loop iteration over arrays with step repetition","description":"Activepieces supports loop steps that iterate over arrays and execute a set of steps for each array element. The loop step receives an array input (from previous step output or flow variable) and repeats the enclosed steps once per element. Each iteration has access to the current element via a loop variable and can access previous iteration results. Loops support break/continue semantics and can be nested to handle multi-dimensional arrays.","intents":["I want to process each item in a list with the same workflow steps","I need to send notifications to multiple recipients in a single workflow","I want to batch API calls for each item in a dataset"],"best_for":["bulk processing workflows (send emails to 100 recipients, update 1000 records)","workflows that need to iterate over paginated API results","data transformation pipelines that process arrays element-by-element"],"limitations":["Loop performance degrades with large arrays (1000+ items) due to sequential execution","No built-in parallelization — loops execute sequentially, not in parallel","Loop variable scope is limited to loop body — cannot access loop variables outside loop"],"requires":["Array input from previous step or flow variable","Loop body steps configured"],"input_types":["Arrays (JSON arrays, step outputs)","Loop iteration count"],"output_types":["Array of step results (one per iteration)","Aggregated results from all iterations"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-activepieces-activepieces__cap_11","uri":"capability://tool.use.integration.mcp.model.context.protocol.server.implementation.for.ai.agent.integration","name":"mcp (model context protocol) server implementation for ai agent integration","description":"Activepieces implements the Model Context Protocol (MCP) specification, exposing workflows and pieces as tools that AI agents (Claude, GPT-4, etc.) can invoke. The MCP server exposes a standardized interface where each workflow or piece becomes a callable tool with input schemas and descriptions. AI agents can discover available tools, invoke them with parameters, and receive results in a structured format. The MCP server handles authentication, input validation, and error handling transparently.","intents":["I want AI agents to invoke my workflows as external tools","I need to expose Activepieces capabilities to Claude or other LLMs via MCP","I want to build AI agent applications that orchestrate Activepieces workflows"],"best_for":["AI agent applications that need to trigger automation workflows","teams building Claude-based assistants that need to perform actions","enterprises integrating Activepieces with AI orchestration platforms"],"limitations":["MCP server requires network connectivity to AI agent host — not suitable for air-gapped environments","Tool invocation latency includes network round-trip time — not suitable for real-time agent interactions","MCP server exposes all workflows as tools — no fine-grained access control per agent"],"requires":["MCP server running (part of Activepieces backend)","AI agent with MCP client support (Claude, GPT-4 with MCP plugin)","Network connectivity between agent and MCP server"],"input_types":["Tool invocation requests from AI agents","Tool parameters matching workflow input schema"],"output_types":["Tool execution results","Workflow output data","Error messages"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-activepieces-activepieces__cap_12","uri":"capability://automation.workflow.webhook.based.flow.triggering.with.payload.validation.and.transformation","name":"webhook-based flow triggering with payload validation and transformation","description":"Activepieces generates unique webhook URLs for each flow that accept HTTP POST requests and trigger flow executions. Webhooks validate incoming payloads against optional JSON schemas and transform payloads before passing them to the flow. The webhook system supports request authentication (API keys, OAuth tokens) and rate limiting to prevent abuse. Webhook payloads are stored in the execution history for debugging and replay purposes.","intents":["I want external services to trigger my workflows via HTTP webhooks","I need to validate incoming webhook payloads before processing","I want to transform webhook data before passing it to workflow steps"],"best_for":["integrations with third-party services that support webhooks (GitHub, Stripe, Slack)","workflows triggered by form submissions or API calls","event-driven architectures where external systems push events to Activepieces"],"limitations":["Webhook URLs are long and difficult to remember — requires documentation or URL shortening","No built-in webhook signature verification — relies on API key authentication","Webhook payloads are limited to HTTP request size (typically 10MB) — not suitable for large file uploads"],"requires":["Publicly accessible Activepieces instance (or reverse proxy for self-hosted)","Webhook URL configured in external service","Optional: API key for webhook authentication"],"input_types":["HTTP POST requests","JSON payloads","Query parameters"],"output_types":["Flow execution ID","Execution status","Webhook response (success/error)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-activepieces-activepieces__cap_13","uri":"capability://automation.workflow.real.time.flow.execution.monitoring.and.debugging.with.step.level.logs","name":"real-time flow execution monitoring and debugging with step-level logs","description":"Activepieces provides a real-time debugging interface that displays step-by-step execution progress, input/output data for each step, and detailed error messages. The system captures logs at each step (piece execution, code execution, router decisions) and streams them to the frontend via WebSocket or polling. Users can inspect intermediate values, understand why a step failed, and replay executions with modified inputs for testing.","intents":["I want to see what data each step in my workflow is processing","I need to debug why a workflow step failed","I want to test workflow changes without running the full flow"],"best_for":["developers building and testing workflows","teams troubleshooting failing workflows in production","workflows with complex data transformations requiring visibility"],"limitations":["Real-time monitoring adds overhead to flow execution — not suitable for high-throughput scenarios","Log retention is limited by database storage — old logs are archived or deleted","Step-level logs are not available for paused executions — only final state is captured"],"requires":["WebSocket support in frontend and backend (or polling fallback)","PostgreSQL for log storage"],"input_types":["Flow execution ID","Step execution events"],"output_types":["Step input/output data","Execution logs","Error messages with stack traces"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-activepieces-activepieces__cap_14","uri":"capability://automation.workflow.error.handling.and.retry.logic.with.exponential.backoff","name":"error handling and retry logic with exponential backoff","description":"Activepieces implements configurable error handling and retry logic at the step level. Each step can be configured with retry policies (max attempts, backoff strategy) that automatically retry failed steps before propagating errors. The system supports exponential backoff with jitter to prevent thundering herd problems. Failed steps can be configured to trigger error handlers (alternative steps) or pause the flow for manual intervention.","intents":["I want transient API failures to be automatically retried","I need to handle errors gracefully without stopping the entire workflow","I want to implement fallback logic when a step fails"],"best_for":["workflows integrating with unreliable external APIs","mission-critical workflows requiring high reliability","workflows with complex error recovery logic"],"limitations":["Retry logic is per-step — no workflow-level retry (must retry from failed step)","Exponential backoff can cause long delays for steps with many retries","Error handlers are limited to alternative steps — no arbitrary error recovery code"],"requires":["Step-level retry configuration","Error handler steps (optional)"],"input_types":["Step failure events","Retry policy configuration"],"output_types":["Retry attempt results","Error handler execution results","Final error if all retries exhausted"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-activepieces-activepieces__cap_2","uri":"capability://text.generation.language.multi.provider.llm.integration.with.structured.data.extraction","name":"multi-provider llm integration with structured data extraction","description":"Activepieces includes native pieces for Claude, OpenAI, Grok, and other LLM providers that enable workflows to invoke language models for text generation, summarization, and structured data extraction. The Claude piece specifically supports JSON schema-based extraction via the tool_use feature, allowing workflows to parse unstructured data into typed objects. LLM pieces handle authentication via API keys stored in the connection management system and support dynamic prompt templating using flow context variables.","intents":["I want to extract structured data from unstructured text using Claude's tool_use capability","I need to generate content or summaries using different LLM providers in the same workflow","I want to use LLM outputs as inputs to subsequent workflow steps"],"best_for":["document processing and data extraction workflows","content generation and summarization automation","AI-powered data enrichment pipelines"],"limitations":["LLM API costs are passed through to user — no built-in cost optimization or caching","Structured extraction requires explicit JSON schema definition — no automatic schema inference","Token limits per request are inherited from provider APIs — no automatic chunking for large documents"],"requires":["API key for target LLM provider (OpenAI, Anthropic, Grok, etc.)","Connection stored in Activepieces connection management system","JSON schema definition for structured extraction (Claude only)"],"input_types":["Text prompts","JSON schema objects","Flow context variables"],"output_types":["Generated text","Structured JSON objects","Extracted data matching schema"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-activepieces-activepieces__cap_3","uri":"capability://code.generation.editing.code.execution.within.workflows.via.sandboxed.javascript.runtime","name":"code execution within workflows via sandboxed javascript runtime","description":"Activepieces allows workflows to include custom JavaScript code steps that execute in an isolated sandbox environment (packages/engine/src/lib/core/code/code-sandbox.ts). The code executor receives the flow execution context (previous step outputs, trigger data, variables) as input and can return arbitrary JSON data to downstream steps. The sandbox uses Node.js VM module or similar isolation mechanism to prevent code from accessing the host filesystem or network directly, though it can invoke other pieces and make API calls through the flow context.","intents":["I want to transform data between workflow steps using custom logic","I need to perform calculations or string manipulations not available in standard pieces","I want to conditionally route flows based on complex business logic"],"best_for":["developers building workflows who need programmatic flexibility","data transformation and ETL workflows requiring custom logic","workflows with complex conditional routing based on multiple variables"],"limitations":["Sandbox isolation adds ~50-100ms latency per code step execution","No access to Node.js filesystem APIs — code cannot read/write files directly","Limited to synchronous code — async/await and promises are not supported in sandbox context","Code size limited to ~50KB per step to prevent memory exhaustion"],"requires":["Node.js 18+ runtime on execution worker","Flow execution context passed to code step","No external npm packages — only JavaScript built-ins available"],"input_types":["JavaScript code string","Flow context object (previous outputs, trigger data)","Step input variables"],"output_types":["JSON-serializable objects","Strings, numbers, booleans","Arrays and nested objects"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-activepieces-activepieces__cap_4","uri":"capability://tool.use.integration.connection.and.authentication.management.with.oauth.and.api.key.support","name":"connection and authentication management with oauth and api key support","description":"Activepieces provides a centralized connection management system (packages/engine/src/lib/services/connections.service.ts) that securely stores and retrieves authentication credentials for external services. Connections support multiple authentication methods: OAuth 2.0 flows with automatic token refresh, API key storage with encryption, basic auth, and custom authentication schemes. Each piece declares its required authentication type, and the flow execution engine injects the appropriate connection credentials when the piece executes. Credentials are encrypted at rest in the database and never exposed to the frontend.","intents":["I want to securely store API keys and OAuth tokens for external services","I need to reuse the same authentication across multiple workflow steps","I want OAuth flows to automatically refresh tokens without manual intervention"],"best_for":["multi-user platforms where credential isolation is critical","workflows integrating with 10+ external services requiring different auth methods","enterprises with security requirements for credential encryption and audit logging"],"limitations":["OAuth token refresh requires background job processing — tokens may expire if worker is down","No built-in credential rotation or expiration policies — relies on external service token lifetimes","Credentials are stored per workspace — no cross-workspace credential sharing"],"requires":["PostgreSQL database for encrypted credential storage","OAuth provider configuration (client ID, client secret, redirect URI)","Encryption key configured in deployment environment"],"input_types":["OAuth authorization codes","API keys","Basic auth username/password","Custom authentication payloads"],"output_types":["Authenticated HTTP headers","Bearer tokens","Connection metadata"],"categories":["tool-use-integration","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-activepieces-activepieces__cap_5","uri":"capability://automation.workflow.queue.based.distributed.flow.execution.with.worker.pool.scaling","name":"queue-based distributed flow execution with worker pool scaling","description":"Activepieces uses a queue-based architecture (packages/server/worker) where flow executions are enqueued as jobs and processed by a pool of worker processes. The system supports multiple queue backends (Redis, in-memory for development) and scales horizontally by adding more worker instances. Each worker dequeues flow execution jobs, loads the flow definition and trigger data, and executes the flow step-by-step using the flow execution engine. The queue system handles job retries, dead-letter queues for failed jobs, and priority-based job scheduling.","intents":["I want to scale workflow execution across multiple machines","I need reliable job processing with automatic retries for transient failures","I want to monitor and debug failed workflow executions"],"best_for":["high-volume automation platforms processing 1000+ flows/minute","distributed deployments across multiple availability zones","teams requiring job-level observability and failure recovery"],"limitations":["Queue latency adds 100-500ms before flow execution starts (job enqueue → dequeue → start)","In-memory queue backend loses jobs on worker restart — not suitable for production","No built-in job prioritization based on SLA — all jobs processed FIFO"],"requires":["Redis 6+ for production queue backend (or in-memory for development)","Worker process running (packages/server/worker)","PostgreSQL for flow definition and execution history storage"],"input_types":["Flow execution job objects","Trigger event payloads","Flow version references"],"output_types":["Job execution status","Step-by-step execution logs","Final flow output data"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-activepieces-activepieces__cap_6","uri":"capability://tool.use.integration.modular.pieces.framework.for.extending.workflow.capabilities","name":"modular pieces framework for extending workflow capabilities","description":"Activepieces provides a pieces framework (packages/pieces/community) where each integration (Discord, Google Drive, Slack, etc.) is a self-contained TypeScript package that exports actions, triggers, and authentication handlers. Pieces follow a standardized interface with input/output schema definitions, error handling, and connection management. The framework includes a piece loader that dynamically imports pieces at runtime and registers them with the flow execution engine. Community members can contribute new pieces via pull requests, and the framework validates piece structure and compatibility.","intents":["I want to add support for a new SaaS tool to Activepieces without modifying core code","I need to create custom pieces for internal APIs and proprietary services","I want to share reusable pieces with my team or the community"],"best_for":["developers extending Activepieces for internal use cases","community contributors adding support for new SaaS integrations","enterprises building custom pieces for proprietary APIs"],"limitations":["Pieces must be TypeScript — no Python or other language support","Piece development requires understanding the Activepieces framework API — steep learning curve","No built-in piece versioning — breaking changes in piece APIs require flow migration"],"requires":["TypeScript 4.9+","Node.js 18+","Understanding of Activepieces piece interface (Property, Action, Trigger types)","API documentation or SDK for the service being integrated"],"input_types":["Piece configuration objects","Action/trigger input schemas","Authentication credentials"],"output_types":["Action execution results","Trigger event payloads","Piece metadata and capabilities"],"categories":["tool-use-integration","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-activepieces-activepieces__cap_7","uri":"capability://automation.workflow.flow.versioning.and.execution.history.with.rollback.capability","name":"flow versioning and execution history with rollback capability","description":"Activepieces maintains a complete version history of flow definitions in the database, allowing users to view, compare, and rollback to previous versions. Each flow execution is recorded with its input data, step-by-step outputs, and final result. The system supports branching flows (creating new versions from old ones) and comparing versions side-by-side. Execution history is queryable by date range, status, and trigger type, enabling debugging and auditing of past runs.","intents":["I want to see what changed in a workflow between versions","I need to rollback a broken workflow to a previous working version","I want to audit all executions of a workflow for compliance purposes"],"best_for":["teams collaborating on workflows where change tracking is critical","regulated industries requiring execution audit trails","workflows in production where rollback capability is essential"],"limitations":["Execution history grows unbounded — requires periodic cleanup or archival","Version comparison is UI-based only — no API for programmatic version diffing","Rollback creates a new version rather than reverting — original version remains in history"],"requires":["PostgreSQL database with sufficient storage for execution history","Flow versioning enabled in backend configuration"],"input_types":["Flow definition objects","Execution trigger data"],"output_types":["Version metadata (timestamp, author, change summary)","Execution records with step outputs","Version diff objects"],"categories":["automation-workflow","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-activepieces-activepieces__cap_8","uri":"capability://automation.workflow.pause.and.resume.flow.execution.with.state.persistence","name":"pause and resume flow execution with state persistence","description":"Activepieces supports pausing flows at specific steps and resuming them later with full state preservation. When a flow is paused, the execution context (all previous step outputs, trigger data, variables) is serialized and stored in the database. When resumed, the context is deserialized and execution continues from the next step. This enables workflows that wait for external approvals, manual interventions, or time-based delays without consuming worker resources.","intents":["I want workflows to pause and wait for manual approval before proceeding","I need to implement time-based delays without blocking worker processes","I want to handle long-running processes that may take hours or days to complete"],"best_for":["approval workflows requiring human intervention","multi-stage processes with time delays between stages","workflows integrating with external systems that have long processing times"],"limitations":["Paused executions consume database storage — no automatic cleanup of old paused states","Resume operations require the same flow version — cannot resume with a different flow definition","No built-in timeout for paused executions — manual cleanup required if resume never happens"],"requires":["PostgreSQL database for execution state storage","API endpoint for triggering resume operations"],"input_types":["Pause trigger (step completion, manual API call)","Resume trigger (approval, timeout, manual API call)"],"output_types":["Paused execution state","Resumed execution results"],"categories":["automation-workflow","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-activepieces-activepieces__cap_9","uri":"capability://automation.workflow.conditional.routing.and.branching.with.dynamic.path.selection","name":"conditional routing and branching with dynamic path selection","description":"Activepieces includes a router step type that enables conditional branching based on flow context variables. The router evaluates conditions (e.g., 'if step1.output > 100') and routes execution to different downstream steps based on the result. Conditions are defined using a simple expression language that supports comparisons, logical operators, and variable interpolation. Multiple conditions can be chained to create complex branching logic, and a default path handles cases where no conditions match.","intents":["I want to execute different steps based on the output of previous steps","I need to implement if/else logic in workflows without writing code","I want to route workflows to different destinations based on data values"],"best_for":["workflows with multiple execution paths based on data conditions","approval workflows that route based on approval status","data validation workflows that handle success/failure cases differently"],"limitations":["Router expression language is limited — no support for complex string operations or regex","No support for nested routers — complex branching requires multiple sequential routers","Router conditions are evaluated sequentially — no parallel branch evaluation"],"requires":["Flow context variables available for condition evaluation","Router step configured with conditions and target steps"],"input_types":["Condition expressions","Flow context variables","Previous step outputs"],"output_types":["Selected execution path","Routed step execution results"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":57,"verified":false,"data_access_risk":"high","permissions":["Node.js 18+ for backend server","PostgreSQL 12+ for flow persistence and execution history","Modern browser with ES2020+ support for flow builder UI","Webhook triggers: publicly accessible API endpoint or ngrok tunnel","Polling triggers: worker process running (packages/server/worker)","Cron triggers: system timezone configuration in deployment environment","Array input from previous step or flow variable","Loop body steps configured","MCP server running (part of Activepieces backend)","AI agent with MCP client support (Claude, GPT-4 with MCP plugin)"],"failure_modes":["Flow complexity is limited by DAG structure — no arbitrary control flow patterns","Visual builder performance degrades with flows exceeding 50+ steps due to frontend rendering","No built-in version control for flows — requires external Git sync integration","Polling triggers have minimum 1-minute interval due to worker scheduling overhead","Webhook triggers require public URL exposure or reverse proxy setup for self-hosted instances","No built-in deduplication — duplicate webhook payloads may trigger multiple executions","Loop performance degrades with large arrays (1000+ items) due to sequential execution","No built-in parallelization — loops execute sequentially, not in parallel","Loop variable scope is limited to loop body — cannot access loop variables outside loop","MCP server requires network connectivity to AI agent host — not suitable for air-gapped environments","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7584549754547247,"quality":0.5,"ecosystem":0.7000000000000001,"match_graph":0.25,"freshness":0.75,"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":"2026-05-24T12:16:22.064Z","last_scraped_at":"2026-05-03T14:23:31.492Z","last_commit":"2026-05-03T14:09:27Z"},"community":{"stars":22022,"forks":3611,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=mcp-activepieces-activepieces","compare_url":"https://unfragile.ai/compare?artifact=mcp-activepieces-activepieces"}},"signature":"bTu++2oyl2+sHfvWxpiO5GVizYALS0xrhNCHyHvWb+15+WClJuIFU9D96wrwuV7hrhBvykV7nNhffU7taiKZBA==","signedAt":"2026-06-20T12:16:27.342Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/mcp-activepieces-activepieces","artifact":"https://unfragile.ai/mcp-activepieces-activepieces","verify":"https://unfragile.ai/api/v1/verify?slug=mcp-activepieces-activepieces","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"}}