{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"smithery_uv6725-homeharvest-mcp","slug":"uv6725-homeharvest-mcp","name":"homeharvest-mcp","type":"mcp","url":"https://github.com/uv6725/homeharvest-mcp","page_url":"https://unfragile.ai/uv6725-homeharvest-mcp","categories":["mcp-servers"],"tags":["mcp","model-context-protocol","smithery:uv6725/homeharvest-mcp"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"smithery_uv6725-homeharvest-mcp__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 the MCP server to invoke functions defined within a schema, integrating seamlessly with multiple AI model providers. It employs a flexible routing mechanism that maps function calls to the appropriate API endpoints based on the defined schema, enabling developers to easily switch between providers like OpenAI and Anthropic without changing the core logic of their applications. This design choice enhances interoperability and reduces vendor lock-in.","intents":["How can I call functions from different AI providers without rewriting my code?","I want to define a schema for my function calls that can work with multiple models.","Can I easily switch between AI models in my application?"],"best_for":["developers building applications that require multi-provider AI integrations"],"limitations":["Requires a well-defined schema for function calls, which may add complexity to initial setup."],"requires":["Node.js 14+","API keys for each AI provider being used"],"input_types":["structured data","text"],"output_types":["structured data","text"],"categories":["tool-use-integration","api orchestration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_uv6725-homeharvest-mcp__cap_1","uri":"capability://memory.knowledge.contextual.state.management.across.function.calls","name":"contextual state management across function calls","description":"This capability enables the MCP server to maintain contextual information across multiple function calls, allowing for richer interactions with AI models. It utilizes a context stack that preserves the state of previous interactions, which can be referenced in subsequent calls. This design choice enhances the coherence of conversations and task execution, making it suitable for complex workflows.","intents":["How can I maintain context between multiple API calls?","I want to create a conversational agent that remembers previous interactions.","Can I manage state across different function calls in my application?"],"best_for":["developers creating conversational agents or complex workflows"],"limitations":["Increased memory usage due to context storage, which may affect performance in high-load scenarios."],"requires":["Node.js 14+","Memory storage solution (e.g., Redis)"],"input_types":["text","structured data"],"output_types":["text","structured data"],"categories":["memory-knowledge","context management"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_uv6725-homeharvest-mcp__cap_2","uri":"capability://data.processing.analysis.dynamic.integration.with.external.data.sources","name":"dynamic integration with external data sources","description":"This capability allows the MCP server to integrate with external data sources dynamically, enabling real-time data retrieval and processing. It uses a plugin architecture that allows developers to define custom connectors for various data sources, such as databases or APIs, which can be invoked during function execution. This flexibility supports a wide range of use cases, from data enrichment to real-time analytics.","intents":["How can I pull in data from external sources during function execution?","I want to enrich my AI model's responses with real-time data.","Can I create custom connectors for my specific data sources?"],"best_for":["developers needing real-time data integration for AI applications"],"limitations":["Custom connector development may require additional time and expertise."],"requires":["Node.js 14+","Access to external data sources"],"input_types":["structured data","text"],"output_types":["structured data","text"],"categories":["data-processing-analysis","etl"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_uv6725-homeharvest-mcp__cap_3","uri":"capability://automation.workflow.asynchronous.task.orchestration","name":"asynchronous task orchestration","description":"This capability enables the MCP server to manage and orchestrate asynchronous tasks across multiple function calls, allowing for non-blocking execution of operations. It employs an event-driven architecture that leverages promises and callbacks to handle task completion and error management, ensuring that the system remains responsive even under heavy loads. This design choice is particularly beneficial for applications requiring high throughput.","intents":["How can I run multiple tasks in parallel without blocking my application?","I want to manage asynchronous operations in my AI workflows.","Can I handle errors gracefully in my asynchronous function calls?"],"best_for":["developers building high-performance applications with asynchronous workflows"],"limitations":["Complexity in managing asynchronous flows may increase development time."],"requires":["Node.js 14+","Promise-based libraries for async handling"],"input_types":["text","structured data"],"output_types":["text","structured data"],"categories":["automation-workflow","orchestration"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":26,"verified":false,"data_access_risk":"moderate","permissions":["Node.js 14+","API keys for each AI provider being used","Memory storage solution (e.g., Redis)","Access to external data sources","Promise-based libraries for async handling"],"failure_modes":["Requires a well-defined schema for function calls, which may add complexity to initial setup.","Increased memory usage due to context storage, which may affect performance in high-load scenarios.","Custom connector development may require additional time and expertise.","Complexity in managing asynchronous flows may increase development time.","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":0.6,"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":"2026-05-24T12:16:28.693Z","last_scraped_at":"2026-05-03T15:19:15.094Z","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=uv6725-homeharvest-mcp","compare_url":"https://unfragile.ai/compare?artifact=uv6725-homeharvest-mcp"}},"signature":"3c8mSpRLpgOSihJxJeMqsMk3O2M/VNwv21qJloPyp9Y5sFe8436iTLLODYkd87BqMs/61htJNMCCqUbRnCKBAw==","signedAt":"2026-06-21T13:25:20.331Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/uv6725-homeharvest-mcp","artifact":"https://unfragile.ai/uv6725-homeharvest-mcp","verify":"https://unfragile.ai/api/v1/verify?slug=uv6725-homeharvest-mcp","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"}}