{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"smithery_mgarlabx-mcp-smithery","slug":"mgarlabx-mcp-smithery","name":"mcp_smithery","type":"mcp","url":"https://github.com/mgarlabx/mcp_smithery","page_url":"https://unfragile.ai/mgarlabx-mcp-smithery","categories":["mcp-servers"],"tags":["mcp","model-context-protocol","smithery:mgarlabx/mcp_smithery"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"smithery_mgarlabx-mcp-smithery__cap_0","uri":"capability://tool.use.integration.multi.provider.model.integration","name":"multi-provider model integration","description":"MCP Smithery facilitates seamless integration with multiple model providers through a unified context protocol. It employs a modular architecture that allows developers to plug in various LLMs and APIs, enabling dynamic switching and orchestration of model calls based on user-defined criteria. This design choice enhances flexibility and reduces vendor lock-in, making it distinct from other MCP implementations.","intents":["How can I integrate multiple AI models into my application?","What is the best way to switch between different model providers dynamically?","Can I orchestrate calls to various APIs using a single protocol?"],"best_for":["developers building applications that require diverse AI model capabilities"],"limitations":["Performance may vary based on the number of integrated models and their response times."],"requires":["Node.js 14+","Access to model provider APIs"],"input_types":["API requests","model context data"],"output_types":["API responses","structured data"],"categories":["tool-use-integration","model-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_mgarlabx-mcp-smithery__cap_1","uri":"capability://memory.knowledge.contextual.state.management","name":"contextual state management","description":"MCP Smithery implements a robust context management system that maintains the state across multiple interactions with different models. It uses a context stack mechanism that preserves relevant information and user inputs, allowing for coherent and contextually aware responses. This capability is crucial for applications requiring continuity in conversations or tasks.","intents":["How can I maintain context across multiple API calls?","What is the best way to manage user state in my application?","Can I ensure that my interactions with models are coherent and contextually relevant?"],"best_for":["developers creating conversational agents or multi-turn applications"],"limitations":["Context management can introduce latency if the context stack grows too large."],"requires":["Node.js 14+","Understanding of context management principles"],"input_types":["user inputs","contextual data"],"output_types":["contextual responses","state information"],"categories":["memory-knowledge","context-management"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_mgarlabx-mcp-smithery__cap_2","uri":"capability://tool.use.integration.dynamic.api.orchestration","name":"dynamic api orchestration","description":"MCP Smithery provides a dynamic API orchestration capability that allows developers to define workflows involving multiple API calls. It uses a declarative syntax for specifying the sequence and conditions under which APIs are called, enabling complex workflows to be executed with minimal overhead. This orchestration is particularly useful for applications that require chaining of model outputs.","intents":["How can I create workflows that involve multiple API calls?","What is the best way to chain outputs from different models?","Can I define conditions for when to call certain APIs?"],"best_for":["developers building complex applications that require multiple API interactions"],"limitations":["Complex workflows may require careful design to avoid performance bottlenecks."],"requires":["Node.js 14+","Familiarity with API orchestration concepts"],"input_types":["workflow definitions","API request data"],"output_types":["chained responses","workflow results"],"categories":["tool-use-integration","workflow-orchestration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_mgarlabx-mcp-smithery__cap_3","uri":"capability://data.processing.analysis.real.time.monitoring.and.logging","name":"real-time monitoring and logging","description":"MCP Smithery includes a built-in real-time monitoring and logging system that tracks API calls, responses, and context changes. This system uses a centralized logging mechanism that aggregates data from all interactions, providing developers with insights into performance and potential issues. This capability is essential for debugging and optimizing applications.","intents":["How can I monitor the performance of my API calls?","What is the best way to log interactions for debugging?","Can I get real-time insights into my application's performance?"],"best_for":["developers needing to troubleshoot and optimize API interactions"],"limitations":["Logging overhead may impact performance if not managed properly."],"requires":["Node.js 14+","Access to logging infrastructure"],"input_types":["API call data","contextual information"],"output_types":["log entries","performance metrics"],"categories":["data-processing-analysis","monitoring"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":25,"verified":false,"data_access_risk":"moderate","permissions":["Node.js 14+","Access to model provider APIs","Understanding of context management principles","Familiarity with API orchestration concepts","Access to logging infrastructure"],"failure_modes":["Performance may vary based on the number of integrated models and their response times.","Context management can introduce latency if the context stack grows too large.","Complex workflows may require careful design to avoid performance bottlenecks.","Logging overhead may impact performance if not managed properly.","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.18,"ecosystem":0.48999999999999994,"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":"2026-05-24T12:16:27.442Z","last_scraped_at":"2026-05-03T15:19:20.346Z","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=mgarlabx-mcp-smithery","compare_url":"https://unfragile.ai/compare?artifact=mgarlabx-mcp-smithery"}},"signature":"80SFa+OG/K6ZAPqELkiAjAtVUUzu9ONhTUVq+QdeLkrOuTbPlYCZfsxIE2krXsl+AvWjyViIawtzdTn3GNXlCw==","signedAt":"2026-06-20T17:45:45.608Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/mgarlabx-mcp-smithery","artifact":"https://unfragile.ai/mgarlabx-mcp-smithery","verify":"https://unfragile.ai/api/v1/verify?slug=mgarlabx-mcp-smithery","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"}}