{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"smithery_heloyom-mcp-server-test","slug":"heloyom-mcp-server-test","name":"mcp-server-test","type":"mcp","url":"https://github.com/HeloyoM/mcp-server-test","page_url":"https://unfragile.ai/heloyom-mcp-server-test","categories":["mcp-servers","testing-quality"],"tags":["mcp","model-context-protocol","smithery:HeloyoM/mcp-server-test"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"smithery_heloyom-mcp-server-test__cap_0","uri":"capability://tool.use.integration.mcp.protocol.integration.for.model.orchestration","name":"mcp protocol integration for model orchestration","description":"This capability allows the server to integrate with various AI models using the Model Context Protocol (MCP), enabling seamless communication and orchestration among different model endpoints. It employs a modular architecture that supports dynamic loading of model plugins, allowing developers to easily extend functionality without modifying the core server code. The server uses a lightweight message broker to handle requests and responses, ensuring low-latency interactions between models and clients.","intents":["How can I integrate multiple AI models into my application using MCP?","What is the best way to orchestrate model calls in a microservices architecture?","Can I dynamically add or remove models from my server setup?"],"best_for":["developers building applications that require multiple AI model integrations"],"limitations":["Limited to models that support the MCP standard; custom models may require additional implementation effort"],"requires":["Node.js 14+","MCP-compliant model endpoints"],"input_types":["text","JSON requests"],"output_types":["JSON responses","structured data"],"categories":["tool-use-integration","model-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_heloyom-mcp-server-test__cap_1","uri":"capability://automation.workflow.real.time.request.handling.with.asynchronous.processing","name":"real-time request handling with asynchronous processing","description":"The server is designed to handle incoming requests asynchronously, leveraging Node.js's event-driven architecture to ensure that multiple requests can be processed simultaneously without blocking. This capability allows the server to efficiently manage high loads, making it suitable for applications requiring real-time interactions. It employs a queueing mechanism to prioritize and manage requests, ensuring that critical tasks are handled promptly.","intents":["How can I ensure my application handles multiple requests without lag?","What techniques can I use for real-time processing of AI model requests?","Can I prioritize certain requests over others in my server?"],"best_for":["teams developing high-performance applications that require real-time processing"],"limitations":["Asynchronous handling may complicate debugging and error tracking; requires careful management of state"],"requires":["Node.js 14+","Express.js or similar framework"],"input_types":["text","JSON requests"],"output_types":["JSON responses","structured data"],"categories":["automation-workflow","performance-optimization"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_heloyom-mcp-server-test__cap_2","uri":"capability://memory.knowledge.dynamic.model.configuration.and.management","name":"dynamic model configuration and management","description":"This capability allows users to configure and manage AI models dynamically through a web interface or API, enabling real-time adjustments to model parameters and settings. The server maintains a centralized configuration store that can be accessed and modified without requiring a server restart, facilitating rapid experimentation and iteration. It also supports versioning of model configurations to track changes over time.","intents":["How can I change model parameters without restarting the server?","What is the best way to manage different versions of model configurations?","Can I experiment with different settings in real-time?"],"best_for":["data scientists and engineers looking to optimize AI model performance"],"limitations":["Real-time changes may introduce instability if not managed properly; requires thorough testing of configurations"],"requires":["Node.js 14+","Database for configuration storage"],"input_types":["text","JSON configurations"],"output_types":["confirmation messages","updated configurations"],"categories":["memory-knowledge","configuration-management"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_heloyom-mcp-server-test__cap_3","uri":"capability://data.processing.analysis.logging.and.monitoring.for.model.performance","name":"logging and monitoring for model performance","description":"This capability provides comprehensive logging and monitoring of model performance metrics, including response times, error rates, and resource utilization. It integrates with popular monitoring tools to visualize data and generate alerts based on predefined thresholds. The logging system is designed to be lightweight and non-intrusive, ensuring minimal impact on model performance while providing valuable insights for optimization.","intents":["How can I monitor the performance of my AI models in real-time?","What metrics should I track to ensure optimal model performance?","Can I set up alerts for when my models exceed certain performance thresholds?"],"best_for":["devops teams and engineers responsible for maintaining AI model performance"],"limitations":["Monitoring overhead may introduce slight latency; requires careful configuration to avoid excessive logging"],"requires":["Node.js 14+","Monitoring tool integration (e.g., Prometheus, Grafana)"],"input_types":["performance metrics","log data"],"output_types":["visual reports","alert notifications"],"categories":["data-processing-analysis","performance-monitoring"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":28,"verified":false,"data_access_risk":"high","permissions":["Node.js 14+","MCP-compliant model endpoints","Express.js or similar framework","Database for configuration storage","Monitoring tool integration (e.g., Prometheus, Grafana)"],"failure_modes":["Limited to models that support the MCP standard; custom models may require additional implementation effort","Asynchronous handling may complicate debugging and error tracking; requires careful management of state","Real-time changes may introduce instability if not managed properly; requires thorough testing of configurations","Monitoring overhead may introduce slight latency; requires careful configuration to avoid excessive logging","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":"2026-05-24T12:16:26.347Z","last_scraped_at":"2026-05-03T15:19:33.056Z","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=heloyom-mcp-server-test","compare_url":"https://unfragile.ai/compare?artifact=heloyom-mcp-server-test"}},"signature":"5//coPrjJuStemiGq5mdjWh5NIM/y0vTcz+CGY64wIpQUIj9RLat05htSYRrOdaZ7d1AHJcBjadL2UXRfun5AQ==","signedAt":"2026-06-22T05:40:12.180Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/heloyom-mcp-server-test","artifact":"https://unfragile.ai/heloyom-mcp-server-test","verify":"https://unfragile.ai/api/v1/verify?slug=heloyom-mcp-server-test","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"}}