{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"smithery_rencosta2025-mcp-server-mas-sequential-thinkingfork","slug":"rencosta2025-mcp-server-mas-sequential-thinkingfork","name":"mcp-server-mas-sequential-thinkingfork","type":"mcp","url":"https://github.com/renCosta2025/mcp-server-mas-sequential-thinkingfork","page_url":"https://unfragile.ai/rencosta2025-mcp-server-mas-sequential-thinkingfork","categories":["mcp-servers"],"tags":["mcp","model-context-protocol","smithery:renCosta2025/mcp-server-mas-sequential-thinkingfork"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"smithery_rencosta2025-mcp-server-mas-sequential-thinkingfork__cap_0","uri":"capability://automation.workflow.mcp.based.sequential.task.orchestration","name":"mcp-based sequential task orchestration","description":"This capability allows for the orchestration of sequential tasks using the Model Context Protocol (MCP), enabling the server to manage and execute tasks in a defined order. It leverages a stateful design to maintain context across multiple task executions, ensuring that each task can access the necessary context from previous tasks. This approach allows for complex workflows to be defined and executed with minimal latency, making it suitable for applications that require sequential processing.","intents":["How can I create a workflow that executes tasks in a specific order?","I need to maintain context between multiple tasks in my application.","How can I ensure that the output of one task feeds into the next?"],"best_for":["developers building complex workflows that require task sequencing"],"limitations":["Limited to sequential task execution; parallel processing is not supported."],"requires":["Node.js 14+","MCP-compatible client library"],"input_types":["structured data","text"],"output_types":["structured data","text"],"categories":["automation-workflow","mcp-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_rencosta2025-mcp-server-mas-sequential-thinkingfork__cap_1","uri":"capability://memory.knowledge.dynamic.context.management","name":"dynamic context management","description":"This capability dynamically manages the context for ongoing tasks by utilizing a context storage mechanism that updates as tasks are executed. It allows for real-time adjustments to the context based on task outputs, enabling more responsive and adaptive workflows. This is achieved through a combination of in-memory storage and persistent state management, which ensures that context is both fast to access and durable across sessions.","intents":["How can I dynamically adjust the context based on task results?","I need a way to store and retrieve context information efficiently.","How can I ensure that my tasks have the latest context available?"],"best_for":["developers needing real-time context updates for task execution"],"limitations":["Context persistence requires additional configuration; default is in-memory only."],"requires":["Node.js 14+","MCP-compatible client library"],"input_types":["text","structured data"],"output_types":["text","structured data"],"categories":["memory-knowledge","context-management"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_rencosta2025-mcp-server-mas-sequential-thinkingfork__cap_2","uri":"capability://tool.use.integration.multi.provider.integration.support","name":"multi-provider integration support","description":"This capability enables integration with multiple external service providers through a unified API interface, allowing users to call functions from various models seamlessly. It employs a plugin architecture that abstracts the specifics of each provider, enabling users to switch or combine services without changing their workflow. This design choice enhances modularity and allows for easy expansion as new providers are added.","intents":["How can I integrate multiple AI models into my workflow?","I need to switch between different service providers without rewriting code.","How do I manage API calls to different models in a single workflow?"],"best_for":["developers integrating multiple AI services into their applications"],"limitations":["Limited to providers that support the MCP; custom integrations may require additional development."],"requires":["Node.js 14+","API keys for each service provider"],"input_types":["text","structured data"],"output_types":["text","structured data"],"categories":["tool-use-integration","api-orchestration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_rencosta2025-mcp-server-mas-sequential-thinkingfork__cap_3","uri":"capability://automation.workflow.sequential.task.logging.and.monitoring","name":"sequential task logging and monitoring","description":"This capability provides detailed logging and monitoring of each task executed within the workflow, allowing developers to track performance and diagnose issues. It utilizes a centralized logging system that captures input, output, and execution time for each task, providing insights into the overall workflow efficiency. This is particularly useful for debugging and optimizing complex workflows.","intents":["How can I monitor the execution of my tasks?","I need to log the inputs and outputs of each step in my workflow.","How can I diagnose performance issues in my task execution?"],"best_for":["developers needing visibility into task execution for debugging"],"limitations":["Logging can introduce overhead; performance may be impacted in high-frequency workflows."],"requires":["Node.js 14+","MCP-compatible client library"],"input_types":["structured data","text"],"output_types":["log files","structured data"],"categories":["automation-workflow","monitoring"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_rencosta2025-mcp-server-mas-sequential-thinkingfork__cap_4","uri":"capability://automation.workflow.error.handling.and.recovery.mechanisms","name":"error handling and recovery mechanisms","description":"This capability implements robust error handling and recovery mechanisms to ensure that workflows can gracefully handle failures. It uses a retry logic combined with fallback strategies to manage errors, allowing workflows to continue or recover from failures without manual intervention. This design choice enhances reliability and user confidence in automated processes.","intents":["How can I ensure my workflow continues despite errors?","I need a way to automatically retry failed tasks.","How can I implement fallback strategies in my workflows?"],"best_for":["developers building resilient workflows that require error management"],"limitations":["Retry logic can lead to increased execution time; careful configuration is required."],"requires":["Node.js 14+","MCP-compatible client library"],"input_types":["structured data","text"],"output_types":["structured data","error logs"],"categories":["automation-workflow","error-handling"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":27,"verified":false,"data_access_risk":"moderate","permissions":["Node.js 14+","MCP-compatible client library","API keys for each service provider"],"failure_modes":["Limited to sequential task execution; parallel processing is not supported.","Context persistence requires additional configuration; default is in-memory only.","Limited to providers that support the MCP; custom integrations may require additional development.","Logging can introduce overhead; performance may be impacted in high-frequency workflows.","Retry logic can lead to increased execution time; careful configuration is required.","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.2,"ecosystem":0.48999999999999994,"match_graph":0.25,"freshness":0.6,"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":"2026-05-24T12:16:28.137Z","last_scraped_at":"2026-05-03T15:19:16.961Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=rencosta2025-mcp-server-mas-sequential-thinkingfork","compare_url":"https://unfragile.ai/compare?artifact=rencosta2025-mcp-server-mas-sequential-thinkingfork"}},"signature":"Gt1wTTxv4Xc7inERkrtOEXl9cxBdabvdUqFWHROMMUaF5Pv0HcLlncOriwDm7U6yBZq8fyuIN9qz4HTie8fKCg==","signedAt":"2026-06-21T05:19:24.610Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/rencosta2025-mcp-server-mas-sequential-thinkingfork","artifact":"https://unfragile.ai/rencosta2025-mcp-server-mas-sequential-thinkingfork","verify":"https://unfragile.ai/api/v1/verify?slug=rencosta2025-mcp-server-mas-sequential-thinkingfork","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"}}