{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"smithery_djyeigner-mcp-test-250911-2","slug":"djyeigner-mcp-test-250911-2","name":"mcp-test-250911-2","type":"mcp","url":"https://github.com/Djyeigner/mcp-test-250911-2","page_url":"https://unfragile.ai/djyeigner-mcp-test-250911-2","categories":["mcp-servers","testing-quality"],"tags":["mcp","model-context-protocol","smithery:Djyeigner/mcp-test-250911-2"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"smithery_djyeigner-mcp-test-250911-2__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 functions using a schema that can be called across multiple model providers, such as OpenAI and Anthropic. It utilizes a flexible function registry that maps function signatures to provider-specific implementations, enabling seamless integration and invocation of functions without needing to alter the calling code. This architecture promotes interoperability and reduces the friction of switching between different AI service providers.","intents":["How can I call functions from different AI providers without changing my code?","What is the best way to manage function signatures for multiple models?","Can I integrate different AI services into my application easily?"],"best_for":["developers integrating multiple AI models into their applications"],"limitations":["Requires manual updates to the function registry when adding new providers","Performance may vary based on the provider's response time"],"requires":["Node.js 14+","API keys for the respective AI providers"],"input_types":["structured data","function signatures"],"output_types":["structured data","response objects"],"categories":["tool-use-integration","api orchestration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_djyeigner-mcp-test-250911-2__cap_1","uri":"capability://planning.reasoning.contextual.model.switching","name":"contextual model switching","description":"This capability enables dynamic switching between different AI models based on the context of the input data. It employs a context analysis layer that evaluates the input and determines the most suitable model to handle the request, optimizing performance and relevance of responses. This design allows for a more adaptive and responsive interaction with AI services, ensuring that the best-suited model is always utilized.","intents":["How can I automatically select the best AI model for different tasks?","What is the best way to improve response relevance by using multiple models?","Can I enhance my application's performance by switching models based on input?"],"best_for":["developers building applications that require diverse AI capabilities"],"limitations":["Context analysis may introduce latency in decision-making","Requires well-defined criteria for model selection"],"requires":["Node.js 14+","API keys for multiple AI providers"],"input_types":["text","structured data"],"output_types":["text","structured data"],"categories":["planning-reasoning","model management"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_djyeigner-mcp-test-250911-2__cap_2","uri":"capability://automation.workflow.asynchronous.request.handling","name":"asynchronous request handling","description":"This capability allows the MCP server to handle multiple requests asynchronously, improving throughput and responsiveness. It uses an event-driven architecture that processes incoming requests in parallel, leveraging non-blocking I/O operations. This design choice ensures that the server can manage high volumes of requests without significant delays, making it suitable for real-time applications.","intents":["How can I improve the responsiveness of my application under heavy load?","What is the best way to handle multiple simultaneous requests to an AI service?","Can I ensure my application remains responsive while processing long-running tasks?"],"best_for":["developers building high-performance applications requiring real-time interactions"],"limitations":["Complexity in managing state across asynchronous calls","Potential for callback hell if not managed properly"],"requires":["Node.js 14+","Properly configured server environment"],"input_types":["text","structured data"],"output_types":["text","structured data"],"categories":["automation-workflow","performance optimization"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_djyeigner-mcp-test-250911-2__cap_3","uri":"capability://data.processing.analysis.real.time.logging.and.monitoring","name":"real-time logging and monitoring","description":"This capability provides real-time logging and monitoring of all requests and responses processed by the MCP server. It integrates with external monitoring tools to provide insights into performance metrics, error rates, and usage patterns. This feature is crucial for maintaining operational visibility and ensuring that any issues can be quickly identified and addressed.","intents":["How can I monitor the performance of my MCP server in real-time?","What is the best way to track errors and usage patterns in my application?","Can I integrate logging with external monitoring tools?"],"best_for":["developers and operators managing production-level applications"],"limitations":["May introduce overhead due to logging operations","Requires configuration of external monitoring tools"],"requires":["Node.js 14+","Access to external monitoring services"],"input_types":["log data","event data"],"output_types":["log entries","performance metrics"],"categories":["data-processing-analysis","monitoring"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":28,"verified":false,"data_access_risk":"moderate","permissions":["Node.js 14+","API keys for the respective AI providers","API keys for multiple AI providers","Properly configured server environment","Access to external monitoring services"],"failure_modes":["Requires manual updates to the function registry when adding new providers","Performance may vary based on the provider's response time","Context analysis may introduce latency in decision-making","Requires well-defined criteria for model selection","Complexity in managing state across asynchronous calls","Potential for callback hell if not managed properly","May introduce overhead due to logging operations","Requires configuration of external monitoring tools","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.18,"ecosystem":0.5900000000000001,"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":"2026-05-24T12:16:26.345Z","last_scraped_at":"2026-05-03T15:19:31.415Z","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=djyeigner-mcp-test-250911-2","compare_url":"https://unfragile.ai/compare?artifact=djyeigner-mcp-test-250911-2"}},"signature":"SmfRCz4wuACIuWkbfILaqHz73GQXwsDa7+ed/+Al+9+wZZL45dxXdFlqz0Op27CgJwtbPngeiUNPVZQIdOOGCQ==","signedAt":"2026-06-22T05:40:28.179Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/djyeigner-mcp-test-250911-2","artifact":"https://unfragile.ai/djyeigner-mcp-test-250911-2","verify":"https://unfragile.ai/api/v1/verify?slug=djyeigner-mcp-test-250911-2","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"}}