{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"smithery_greatsumini-nanobanana-api-mcp","slug":"greatsumini-nanobanana-api-mcp","name":"nanobanana-api-mcp","type":"mcp","url":"https://github.com/greatSumini/nanobanana-api-mcp","page_url":"https://unfragile.ai/greatsumini-nanobanana-api-mcp","categories":["mcp-servers"],"tags":["mcp","model-context-protocol","smithery:greatSumini/nanobanana-api-mcp"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"smithery_greatsumini-nanobanana-api-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 users to define functions using a schema that can be called across multiple AI service providers. It utilizes a modular architecture that abstracts the function calling mechanism, enabling seamless integration with various APIs such as OpenAI and Anthropic. The design choice to implement a schema-based approach ensures that function definitions are consistent and easily maintainable, allowing for dynamic updates and provider switching without code changes.","intents":["How can I call functions from different AI providers without changing my code?","I need a consistent way to define and manage API calls across multiple services.","Can I switch AI providers easily in my application?"],"best_for":["developers building applications that leverage multiple AI models"],"limitations":["Requires manual updates to the schema when adding new functions","Limited to supported providers listed in the documentation"],"requires":["Node.js 14+","API keys for the respective AI providers"],"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_greatsumini-nanobanana-api-mcp__cap_1","uri":"capability://memory.knowledge.contextual.request.handling","name":"contextual request handling","description":"This capability enables the server to manage and maintain context across multiple requests, allowing for more coherent interactions with the AI models. It employs a context management system that tracks user sessions and retains relevant information, which is passed along with each API call. This design choice enhances the user experience by ensuring that the AI can respond in a contextually aware manner, making conversations feel more natural and relevant.","intents":["How can I maintain context in my interactions with AI models?","I want my application to remember user preferences across sessions.","Can I create a more conversational experience with my AI integration?"],"best_for":["developers creating chatbots or interactive AI applications"],"limitations":["Context retention is limited to session duration","Requires additional memory management for long-term context"],"requires":["Node.js 14+","Session management library"],"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_greatsumini-nanobanana-api-mcp__cap_2","uri":"capability://tool.use.integration.dynamic.api.routing","name":"dynamic api routing","description":"This capability allows the MCP server to dynamically route requests to the appropriate AI model based on the input type and user-defined criteria. It employs a routing layer that analyzes incoming requests and determines the best model to handle each request, optimizing for performance and response accuracy. This architecture enables developers to easily extend the system by adding new models without disrupting existing functionality.","intents":["How can I route requests to different AI models based on input?","I need to optimize my API calls for performance and accuracy.","Can I easily add new models to my existing setup?"],"best_for":["developers integrating multiple AI models into their applications"],"limitations":["Routing logic can become complex with many models","Requires thorough documentation to manage routing rules"],"requires":["Node.js 14+","Configuration files for routing rules"],"input_types":["text","structured data"],"output_types":["text","structured data"],"categories":["tool-use-integration","api orchestration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_greatsumini-nanobanana-api-mcp__cap_3","uri":"capability://automation.workflow.multi.threaded.request.processing","name":"multi-threaded request processing","description":"This capability enables the MCP server to handle multiple requests simultaneously through a multi-threaded architecture. By leveraging asynchronous processing and worker threads, the server can efficiently manage high volumes of requests without blocking, ensuring fast response times. This design choice is particularly beneficial for applications that require real-time interactions with AI models, as it minimizes latency and improves overall throughput.","intents":["How can I improve the performance of my API under heavy load?","I need to handle multiple user requests at the same time.","Can I ensure fast response times for my AI application?"],"best_for":["developers building high-performance AI applications"],"limitations":["Increased complexity in managing thread safety","Potential for resource contention under extreme loads"],"requires":["Node.js 14+","Proper configuration of worker threads"],"input_types":["text","structured data"],"output_types":["text","structured data"],"categories":["automation-workflow","orchestration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_greatsumini-nanobanana-api-mcp__cap_4","uri":"capability://data.processing.analysis.real.time.logging.and.monitoring","name":"real-time logging and monitoring","description":"This capability provides developers with real-time logging and monitoring of API requests and responses, allowing for immediate feedback and troubleshooting. It integrates with popular logging frameworks to capture detailed metrics and logs, which can be analyzed to optimize performance and identify issues. The choice to implement real-time monitoring ensures that developers can maintain high availability and reliability of their applications.","intents":["How can I monitor my API's performance in real-time?","I need to troubleshoot issues as they happen.","Can I get insights into user interactions with my AI models?"],"best_for":["developers needing to maintain high reliability in their applications"],"limitations":["Logging can introduce overhead if not managed properly","Requires additional setup for monitoring tools"],"requires":["Node.js 14+","Logging framework integration"],"input_types":["text","structured data"],"output_types":["logs","metrics"],"categories":["data-processing-analysis","monitoring"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":27,"verified":false,"data_access_risk":"moderate","permissions":["Node.js 14+","API keys for the respective AI providers","Session management library","Configuration files for routing rules","Proper configuration of worker threads","Logging framework integration"],"failure_modes":["Requires manual updates to the schema when adding new functions","Limited to supported providers listed in the documentation","Context retention is limited to session duration","Requires additional memory management for long-term context","Routing logic can become complex with many models","Requires thorough documentation to manage routing rules","Increased complexity in managing thread safety","Potential for resource contention under extreme loads","Logging can introduce overhead if not managed properly","Requires additional setup for monitoring tools","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.2,"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":"2026-05-24T12:16:26.347Z","last_scraped_at":"2026-05-03T15:19:16.961Z","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=greatsumini-nanobanana-api-mcp","compare_url":"https://unfragile.ai/compare?artifact=greatsumini-nanobanana-api-mcp"}},"signature":"tAJRxXO7RmvownrHzW/wbgRtIW8nFLCpIo4ecQOiIQca02wIHibQMViHi0Y9G6T/SpppzQV5W8pXpGSf69W+Dg==","signedAt":"2026-06-21T15:56:01.493Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/greatsumini-nanobanana-api-mcp","artifact":"https://unfragile.ai/greatsumini-nanobanana-api-mcp","verify":"https://unfragile.ai/api/v1/verify?slug=greatsumini-nanobanana-api-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"}}