{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"smithery_learnnaga-ai-103","slug":"learnnaga-ai-103","name":"ai-103","type":"mcp","url":"https://smithery.ai/servers/learnnaga/ai-103","page_url":"https://unfragile.ai/learnnaga-ai-103","categories":["mcp-servers"],"tags":["mcp","model-context-protocol","smithery:learnnaga/ai-103"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"smithery_learnnaga-ai-103__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 developers to define functions using a schema that can be called across multiple AI model providers. It utilizes a standardized protocol for function definitions, enabling seamless integration with various APIs such as OpenAI and Anthropic. The architecture is designed to abstract the underlying API differences, allowing for a unified interface for function invocation, which enhances flexibility and reduces integration complexity.","intents":["How can I call functions from different AI models without rewriting my code?","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 multi-provider AI integrations"],"limitations":["Requires a well-defined schema for function calls, which may add complexity for simple use cases."],"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","api-orchestration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_learnnaga-ai-103__cap_1","uri":"capability://tool.use.integration.context.aware.api.orchestration","name":"context-aware api orchestration","description":"This capability enables the orchestration of API calls with context management, allowing for dynamic adjustments based on the current state or previous interactions. It employs a context management layer that tracks user interactions and adjusts API calls accordingly, ensuring that the responses are relevant and contextually appropriate. This design enhances user experience by maintaining continuity in interactions.","intents":["How can I maintain context between multiple API calls in my application?","I want to ensure my API responses are relevant to the user's previous inputs.","What is the best way to manage state across different API interactions?"],"best_for":["developers creating conversational agents or interactive applications"],"limitations":["Context management may introduce latency in response times due to state tracking."],"requires":["Node.js 18+","API key for the orchestration service"],"input_types":["text","structured data"],"output_types":["text","structured data"],"categories":["tool-use-integration","context-management"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_learnnaga-ai-103__cap_2","uri":"capability://tool.use.integration.multi.model.response.aggregation","name":"multi-model response aggregation","description":"This capability aggregates responses from multiple AI models into a single coherent output. It employs a response aggregation layer that evaluates and combines outputs based on predefined criteria such as relevance, confidence, and context. This approach allows developers to leverage the strengths of different models simultaneously, providing richer and more nuanced responses to user queries.","intents":["How can I combine outputs from different AI models for better results?","I want to leverage multiple AI models to enhance my application's responses.","What is the best way to aggregate responses from various AI services?"],"best_for":["developers looking to enhance response quality by using multiple AI models"],"limitations":["Aggregation logic can become complex and may require fine-tuning for optimal results."],"requires":["Node.js 18+","API keys for the respective AI models"],"input_types":["text","structured data"],"output_types":["text","structured data"],"categories":["tool-use-integration","response-aggregation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_learnnaga-ai-103__cap_3","uri":"capability://automation.workflow.dynamic.error.handling.and.fallback.mechanisms","name":"dynamic error handling and fallback mechanisms","description":"This capability implements dynamic error handling strategies that allow the system to gracefully manage API failures or unexpected responses. It utilizes a fallback mechanism that can switch to alternative models or predefined responses based on the nature of the error encountered. This design ensures higher reliability and user satisfaction by minimizing disruptions during interactions.","intents":["How can I ensure my application remains functional during API failures?","I want to implement fallback strategies for handling errors in API calls.","What is the best way to manage unexpected responses from AI services?"],"best_for":["developers building resilient applications that rely on external APIs"],"limitations":["Fallback mechanisms may lead to less optimal responses if not well-defined."],"requires":["Node.js 18+","API keys for the respective AI services"],"input_types":["text","structured data"],"output_types":["text","structured data"],"categories":["automation-workflow","error-handling"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":31,"verified":false,"data_access_risk":"moderate","permissions":["Node.js 18+","API keys for the respective AI providers","API key for the orchestration service","API keys for the respective AI models","API keys for the respective AI services"],"failure_modes":["Requires a well-defined schema for function calls, which may add complexity for simple use cases.","Context management may introduce latency in response times due to state tracking.","Aggregation logic can become complex and may require fine-tuning for optimal results.","Fallback mechanisms may lead to less optimal responses if not well-defined.","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.3524832808328236,"quality":0.18,"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":"2026-05-24T12:16:26.915Z","last_scraped_at":"2026-05-03T15:18:35.216Z","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=learnnaga-ai-103","compare_url":"https://unfragile.ai/compare?artifact=learnnaga-ai-103"}},"signature":"O3fGQIfyxFSsyVctBFzp1jglqZhRn9qxY1lTYEeoEejByMxs/lDHbZ/qVSzvVLXF0dwnM4MTCZX2D4hYJoQ4Dg==","signedAt":"2026-06-21T00:20:12.731Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/learnnaga-ai-103","artifact":"https://unfragile.ai/learnnaga-ai-103","verify":"https://unfragile.ai/api/v1/verify?slug=learnnaga-ai-103","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"}}