{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"smithery_rafaelldanieli-tentra","slug":"rafaelldanieli-tentra","name":"tentra","type":"mcp","url":"https://trytentra.com/","page_url":"https://unfragile.ai/rafaelldanieli-tentra","categories":["mcp-servers"],"tags":["mcp","model-context-protocol","smithery:rafaelldanieli/tentra"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"smithery_rafaelldanieli-tentra__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":"Tentra implements a schema-based function calling mechanism that allows users to define and invoke functions across multiple AI model providers seamlessly. This is achieved through a unified API layer that abstracts the differences between providers, enabling developers to switch or combine models without changing their codebase. The architecture leverages a plugin system that dynamically loads provider-specific modules, ensuring flexibility and extensibility.","intents":["How can I call functions from different AI model providers without changing my code?","I want to integrate multiple AI services into my application easily.","How do I manage function calls across different AI models in a consistent way?"],"best_for":["developers building applications that require multi-provider AI integrations"],"limitations":["Limited to providers that conform to the defined schema; custom providers may require additional work."],"requires":["Node.js 14+","Access to the respective AI model APIs"],"input_types":["structured data","function definitions"],"output_types":["structured data","response objects"],"categories":["tool-use-integration","api orchestration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_rafaelldanieli-tentra__cap_1","uri":"capability://memory.knowledge.contextual.model.switching","name":"contextual model switching","description":"Tentra supports contextual model switching based on user-defined parameters, allowing applications to select the most appropriate AI model for a given task dynamically. This is achieved through a context management layer that evaluates the input data and selects the model that best fits the context, improving response relevance and accuracy. The implementation uses a lightweight decision engine that can be extended with custom logic.","intents":["How can I automatically choose the best AI model based on the input context?","I want to improve the accuracy of my AI responses by selecting models dynamically.","How do I implement a system that adapts to different user needs in real-time?"],"best_for":["teams developing adaptive AI applications that need to optimize performance based on context"],"limitations":["Requires careful definition of context parameters; misconfiguration can lead to suboptimal model selection."],"requires":["Node.js 14+","Defined context parameters for model selection"],"input_types":["text","user input"],"output_types":["text","model responses"],"categories":["memory-knowledge","context management"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_rafaelldanieli-tentra__cap_2","uri":"capability://automation.workflow.real.time.api.orchestration","name":"real-time api orchestration","description":"Tentra provides real-time API orchestration capabilities that enable the chaining of multiple API calls into a single workflow. This is facilitated through an event-driven architecture that listens for events and triggers subsequent API calls based on responses. The system supports both synchronous and asynchronous workflows, allowing for complex interactions with minimal latency.","intents":["How can I create workflows that involve multiple API calls in real-time?","I want to orchestrate different services to respond to user actions seamlessly.","How do I handle asynchronous API responses in my application?"],"best_for":["developers building applications that require complex API interactions"],"limitations":["Increased complexity in managing state across asynchronous calls; requires robust error handling."],"requires":["Node.js 14+","Access to the APIs being orchestrated"],"input_types":["event data","API request payloads"],"output_types":["API responses","aggregated results"],"categories":["automation-workflow","orchestration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_rafaelldanieli-tentra__cap_3","uri":"capability://data.processing.analysis.dynamic.data.transformation","name":"dynamic data transformation","description":"Tentra includes capabilities for dynamic data transformation, allowing users to define transformation rules that can be applied to incoming data before it is processed by AI models. This is achieved through a rule-based engine that interprets transformation scripts and applies them in real-time, ensuring that data is in the correct format for each model. The implementation supports a variety of data formats and transformation types.","intents":["How can I preprocess data before sending it to an AI model?","I need to transform incoming data into a format suitable for my AI applications.","How do I apply custom transformation rules to my data dynamically?"],"best_for":["data engineers and developers working with diverse data sources"],"limitations":["Complex transformation rules may introduce processing overhead; requires thorough testing."],"requires":["Node.js 14+","Defined transformation rules"],"input_types":["structured data","unstructured data"],"output_types":["transformed data","structured output"],"categories":["data-processing-analysis","transformation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_rafaelldanieli-tentra__cap_4","uri":"capability://automation.workflow.integrated.monitoring.and.logging","name":"integrated monitoring and logging","description":"Tentra provides integrated monitoring and logging capabilities that allow developers to track API usage, performance metrics, and error rates in real-time. This is accomplished through a centralized logging service that aggregates logs from all components of the system, enabling easy access to performance data and troubleshooting information. The architecture supports customizable logging levels and formats.","intents":["How can I monitor the performance of my API calls in real-time?","I want to log errors and usage statistics for my application.","How do I set up logging for different components of my system?"],"best_for":["developers needing visibility into API performance and error handling"],"limitations":["Logging overhead may impact performance; requires careful configuration to avoid excessive log data."],"requires":["Node.js 14+","Access to logging configuration settings"],"input_types":["event data","API responses"],"output_types":["log entries","performance reports"],"categories":["automation-workflow","monitoring"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":29,"verified":false,"data_access_risk":"moderate","permissions":["Node.js 14+","Access to the respective AI model APIs","Defined context parameters for model selection","Access to the APIs being orchestrated","Defined transformation rules","Access to logging configuration settings"],"failure_modes":["Limited to providers that conform to the defined schema; custom providers may require additional work.","Requires careful definition of context parameters; misconfiguration can lead to suboptimal model selection.","Increased complexity in managing state across asynchronous calls; requires robust error handling.","Complex transformation rules may introduce processing overhead; requires thorough testing.","Logging overhead may impact performance; requires careful configuration to avoid excessive log data.","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":0.9,"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":"2026-05-03T15:19:39.638Z","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=rafaelldanieli-tentra","compare_url":"https://unfragile.ai/compare?artifact=rafaelldanieli-tentra"}},"signature":"L252+8ZQ5wX8SS0YDOEQuAi0wYCzHWy5gJilucDB4XNOTqtUyvSQs45qZXgvt4y/O6VjQkKEhefJO/jrXslRBg==","signedAt":"2026-06-16T07:42:01.625Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/rafaelldanieli-tentra","artifact":"https://unfragile.ai/rafaelldanieli-tentra","verify":"https://unfragile.ai/api/v1/verify?slug=rafaelldanieli-tentra","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"}}