{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"smithery_stefensuhat-sandbox-sapa-ai","slug":"stefensuhat-sandbox-sapa-ai","name":"sandbox-sapa-ai","type":"mcp","url":"https://smithery.ai/servers/stefensuhat/sandbox-sapa-ai","page_url":"https://unfragile.ai/stefensuhat-sandbox-sapa-ai","categories":["mcp-servers"],"tags":["mcp","model-context-protocol","smithery:stefensuhat/sandbox-sapa-ai"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"smithery_stefensuhat-sandbox-sapa-ai__cap_0","uri":"capability://tool.use.integration.schema.based.function.calling.with.multi.provider.support","name":"schema-based function calling with multi-provider support","description":"This capability allows users to define and invoke functions through a schema-based registry that supports multiple AI model providers. It integrates seamlessly with the Model Context Protocol (MCP), enabling dynamic function resolution based on the context and capabilities of the selected model. The architecture leverages a modular design that allows for easy addition of new providers without disrupting existing functionality.","intents":["How can I call functions from different AI models using a unified interface?","I need to integrate multiple AI services into my application efficiently.","What is the best way to manage function calls across different AI providers?"],"best_for":["developers building applications that require integration with multiple AI models"],"limitations":["Requires a well-defined schema for function calls, which may increase initial setup time.","Performance may vary based on the number of providers integrated."],"requires":["Node.js 18+","API keys for the respective AI providers"],"input_types":["structured data","text"],"output_types":["structured data","text"],"categories":["tool-use-integration","mcp-servers"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_stefensuhat-sandbox-sapa-ai__cap_1","uri":"capability://memory.knowledge.contextual.model.switching","name":"contextual model switching","description":"This capability enables the system to switch between different AI models based on the context of the request. It uses a context-aware routing mechanism that analyzes input data and selects the most appropriate model for the task at hand. This approach enhances the efficiency and relevance of responses by leveraging the strengths of each model in specific scenarios.","intents":["How can I optimize my application to use the best AI model for each task?","I want to improve response accuracy by selecting models based on user input.","What is the best way to handle diverse tasks with different AI capabilities?"],"best_for":["teams developing applications that require varied AI functionalities"],"limitations":["Context analysis may introduce latency in response times.","Requires careful tuning of context parameters for optimal performance."],"requires":["Node.js 18+","Access to multiple AI models"],"input_types":["text","structured data"],"output_types":["text","structured data"],"categories":["memory-knowledge","mcp-servers"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_stefensuhat-sandbox-sapa-ai__cap_2","uri":"capability://automation.workflow.integrated.logging.and.monitoring","name":"integrated logging and monitoring","description":"This capability provides comprehensive logging and monitoring of all interactions with the AI models and functions. It captures detailed metrics and logs for each request, including response times and success rates, which can be analyzed for performance optimization. The architecture uses a centralized logging service that aggregates data from all components, making it easy to track and troubleshoot issues.","intents":["How can I monitor the performance of my AI integrations?","I need to troubleshoot issues with my AI model interactions.","What tools can I use to analyze the effectiveness of my AI functions?"],"best_for":["developers and operations teams managing AI-driven applications"],"limitations":["Logging overhead may introduce slight performance degradation.","Requires additional setup for log storage and analysis."],"requires":["Node.js 18+","Access to a logging service or database"],"input_types":["text","structured data"],"output_types":["logs","metrics"],"categories":["automation-workflow","mcp-servers"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_stefensuhat-sandbox-sapa-ai__cap_3","uri":"capability://text.generation.language.dynamic.response.generation","name":"dynamic response generation","description":"This capability enables the generation of responses that adapt based on user interactions and context. It employs a feedback loop mechanism that learns from previous interactions to improve response quality over time. The architecture supports real-time updates to the response generation logic, allowing for continuous improvement based on user feedback and performance metrics.","intents":["How can I create more personalized responses for my users?","I want my application to learn from user interactions to improve over time.","What methods can I use to enhance the quality of AI-generated responses?"],"best_for":["developers creating conversational agents or interactive applications"],"limitations":["Requires ongoing data collection and analysis to improve response quality.","May introduce complexity in managing feedback loops."],"requires":["Node.js 18+","Access to user interaction data"],"input_types":["text","user feedback"],"output_types":["text","adapted responses"],"categories":["text-generation-language","mcp-servers"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_stefensuhat-sandbox-sapa-ai__cap_4","uri":"capability://data.processing.analysis.multi.format.data.handling","name":"multi-format data handling","description":"This capability allows the system to process and respond to inputs in various formats, including text, structured data, and even multimedia. It employs a flexible parsing engine that can interpret different input types and convert them into a unified format for processing. This architecture supports a wide range of applications, from chatbots to data analysis tools, by accommodating diverse user needs.","intents":["How can I handle different types of input in my AI application?","I need to support both text and structured data in my interactions.","What is the best way to manage multimedia inputs in my AI models?"],"best_for":["developers building versatile AI applications that require multi-format support"],"limitations":["Complexity in handling format conversions may lead to increased processing time.","Requires thorough testing to ensure compatibility across formats."],"requires":["Node.js 18+","Libraries for handling specific data formats"],"input_types":["text","structured data","multimedia"],"output_types":["text","structured data","multimedia"],"categories":["data-processing-analysis","mcp-servers"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":24,"verified":false,"data_access_risk":"high","permissions":["Node.js 18+","API keys for the respective AI providers","Access to multiple AI models","Access to a logging service or database","Access to user interaction data","Libraries for handling specific data formats"],"failure_modes":["Requires a well-defined schema for function calls, which may increase initial setup time.","Performance may vary based on the number of providers integrated.","Context analysis may introduce latency in response times.","Requires careful tuning of context parameters for optimal performance.","Logging overhead may introduce slight performance degradation.","Requires additional setup for log storage and analysis.","Requires ongoing data collection and analysis to improve response quality.","May introduce complexity in managing feedback loops.","Complexity in handling format conversions may lead to increased processing time.","Requires thorough testing to ensure compatibility across formats.","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.2,"ecosystem":0.38999999999999996,"match_graph":0.25,"freshness":0.5,"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":"2026-05-24T12:16:28.139Z","last_scraped_at":"2026-05-03T15:19:15.091Z","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=stefensuhat-sandbox-sapa-ai","compare_url":"https://unfragile.ai/compare?artifact=stefensuhat-sandbox-sapa-ai"}},"signature":"yqw2eAE+w3pzyGe6itinjGl9ZoRMx+Yt+OgLpdxyWOUxTiLpzOw5Rt0bENPPPHNJ+HrcZTnuNCEZkeExlE1+BQ==","signedAt":"2026-06-20T12:29:09.264Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/stefensuhat-sandbox-sapa-ai","artifact":"https://unfragile.ai/stefensuhat-sandbox-sapa-ai","verify":"https://unfragile.ai/api/v1/verify?slug=stefensuhat-sandbox-sapa-ai","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"}}