{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"smithery_pertrelly-aifirst","slug":"pertrelly-aifirst","name":"aifirst","type":"mcp","url":"https://smithery.ai/servers/pertrelly/aifirst","page_url":"https://unfragile.ai/pertrelly-aifirst","categories":["mcp-servers"],"tags":["mcp","model-context-protocol","smithery:pertrelly/aifirst"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"smithery_pertrelly-aifirst__cap_0","uri":"capability://memory.knowledge.model.context.management","name":"model context management","description":"This capability manages the context for multiple models using a centralized context registry that allows for dynamic updates and retrieval of context data. It employs a publish-subscribe pattern to ensure that changes in context are propagated to all active model instances in real-time, enabling seamless integration across different models and applications. This architecture allows for efficient context switching and management, which is particularly useful in multi-model environments.","intents":["How can I manage context for multiple AI models in my application?","What is the best way to update context data dynamically for various models?","How do I ensure that all models have access to the latest context information?"],"best_for":["developers building applications that utilize multiple AI models"],"limitations":["Requires careful management of context updates to avoid stale data issues","Performance may degrade with excessive context changes"],"requires":["Node.js 14+","Access to the MCP server"],"input_types":["structured data","text"],"output_types":["structured data","text"],"categories":["memory-knowledge","model-management"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_pertrelly-aifirst__cap_1","uri":"capability://tool.use.integration.api.orchestration.for.model.integration","name":"api orchestration for model integration","description":"This capability allows for seamless orchestration of API calls to various AI models through a unified interface, enabling developers to easily integrate and switch between different models. It leverages a schema-based approach to define API contracts, ensuring that all interactions are consistent and well-defined. This architecture simplifies the integration process and reduces the overhead typically associated with managing multiple API endpoints.","intents":["How can I integrate multiple AI models into my application easily?","What is the best way to manage API calls to different AI services?","How do I ensure consistent data formats across different model APIs?"],"best_for":["developers integrating multiple AI services into a single application"],"limitations":["Limited to models that comply with the defined API schema","May require additional configuration for new models"],"requires":["Node.js 14+","Access to the MCP server"],"input_types":["API requests","structured data"],"output_types":["API responses","structured data"],"categories":["tool-use-integration","api-management"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_pertrelly-aifirst__cap_2","uri":"capability://planning.reasoning.dynamic.model.switching","name":"dynamic model switching","description":"This capability enables applications to dynamically switch between different AI models based on user input or context changes. It uses a decision-making engine that evaluates the current context and user intent to determine the most appropriate model to invoke. This architecture allows for greater flexibility and responsiveness in applications that require real-time decision-making.","intents":["How can I switch between different AI models based on user input?","What is the best way to adapt my application to different contexts dynamically?","How do I implement a decision-making process for model selection?"],"best_for":["developers creating adaptive AI applications that respond to user needs"],"limitations":["Decision-making logic must be carefully designed to avoid incorrect model selection","Increased complexity in application design"],"requires":["Node.js 14+","Access to the MCP server"],"input_types":["user input","context data"],"output_types":["model responses","structured data"],"categories":["planning-reasoning","model-management"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_pertrelly-aifirst__cap_3","uri":"capability://data.processing.analysis.contextual.data.transformation","name":"contextual data transformation","description":"This capability transforms input data based on the current context before passing it to the AI models. It uses a set of predefined transformation rules that can be dynamically updated based on context changes, ensuring that the data is always in the optimal format for the selected model. This approach minimizes the risk of errors due to format mismatches and enhances the overall performance of the AI system.","intents":["How can I ensure my input data is correctly formatted for different models?","What is the best way to apply transformations to data based on context?","How do I manage data formatting dynamically in my application?"],"best_for":["developers needing to preprocess data for multiple AI models"],"limitations":["Transformation rules must be maintained and updated regularly","Performance can be impacted by complex transformation logic"],"requires":["Node.js 14+","Access to the MCP server"],"input_types":["structured data","text"],"output_types":["transformed data","structured data"],"categories":["data-processing-analysis","data-transformation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_pertrelly-aifirst__cap_4","uri":"capability://data.processing.analysis.real.time.context.analytics","name":"real-time context analytics","description":"This capability provides analytics on context usage and model performance in real-time, allowing developers to monitor how context changes affect model outputs. It employs a logging and metrics collection system that captures relevant data points and provides insights through a dashboard interface. This enables proactive adjustments to context management strategies based on observed performance metrics.","intents":["How can I monitor the performance of my AI models in real-time?","What insights can I gain from context usage analytics?","How do I adjust my context management based on performance data?"],"best_for":["developers looking to optimize AI model performance through analytics"],"limitations":["Requires integration with analytics tools for advanced reporting","Potential performance overhead from logging"],"requires":["Node.js 14+","Access to the MCP server"],"input_types":["context data","model outputs"],"output_types":["analytics reports","structured data"],"categories":["data-processing-analysis","analytics"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":24,"verified":false,"data_access_risk":"moderate","permissions":["Node.js 14+","Access to the MCP server"],"failure_modes":["Requires careful management of context updates to avoid stale data issues","Performance may degrade with excessive context changes","Limited to models that comply with the defined API schema","May require additional configuration for new models","Decision-making logic must be carefully designed to avoid incorrect model selection","Increased complexity in application design","Transformation rules must be maintained and updated regularly","Performance can be impacted by complex transformation logic","Requires integration with analytics tools for advanced reporting","Potential performance overhead from logging","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":"2026-05-24T12:16:27.443Z","last_scraped_at":"2026-05-03T15:19:24.054Z","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=pertrelly-aifirst","compare_url":"https://unfragile.ai/compare?artifact=pertrelly-aifirst"}},"signature":"PshnCKKFaOwJ18LMILOQ+DXKmsFc2L0NHZZJ7cRQyZP5v91J9MGkl8CEvTjjIge1/R37X6PjQlNLzZ1f1uYlCA==","signedAt":"2026-06-21T00:16:45.982Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/pertrelly-aifirst","artifact":"https://unfragile.ai/pertrelly-aifirst","verify":"https://unfragile.ai/api/v1/verify?slug=pertrelly-aifirst","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"}}