{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"smithery_vibecode-mcp-prompt","slug":"vibecode-mcp-prompt","name":"prompt-refiner","type":"mcp","url":"https://github.com/saurabhjambure-pixel/vibe-prompt-mcp","page_url":"https://unfragile.ai/vibecode-mcp-prompt","categories":["mcp-servers"],"tags":["mcp","model-context-protocol","smithery:vibecode-mcp/prompt"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"smithery_vibecode-mcp-prompt__cap_0","uri":"capability://text.generation.language.dynamic.prompt.refinement","name":"dynamic prompt refinement","description":"This capability allows users to iteratively refine prompts for language models by leveraging a feedback loop that incorporates user input and model responses. It uses a context-aware architecture that adapts prompts based on previous interactions, ensuring that the generated outputs align closely with user expectations. The integration with the Model Context Protocol (MCP) enables seamless communication between the prompt-refiner and various language models, enhancing the overall user experience.","intents":["How can I improve the quality of my prompts for better model responses?","I want to refine my prompts based on previous outputs to achieve more accurate results.","Can I adjust my prompts dynamically during a session to see real-time improvements?"],"best_for":["developers building applications that require iterative prompt adjustments"],"limitations":["Requires continuous user input for effective refinement, which may not suit all use cases."],"requires":["Node.js 14+","MCP-compatible language model API key"],"input_types":["text"],"output_types":["text"],"categories":["text-generation-language","prompt-engineering"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_vibecode-mcp-prompt__cap_1","uri":"capability://tool.use.integration.multi.model.integration.support","name":"multi-model integration support","description":"This capability enables the prompt-refiner to connect and interact with multiple language models through a unified MCP interface. By abstracting the model-specific details, it allows users to switch between different models seamlessly, facilitating experimentation and comparison of outputs. The architecture supports dynamic model selection based on user-defined criteria, enhancing flexibility in prompt refinement processes.","intents":["How can I test my prompts across different language models?","I want to switch between models easily to compare their responses to the same prompt.","Can I integrate multiple models into my workflow for diverse output generation?"],"best_for":["data scientists and developers exploring various language models"],"limitations":["Performance may vary based on the number of models integrated and their individual latencies."],"requires":["API keys for each integrated model","Node.js 14+"],"input_types":["text"],"output_types":["text"],"categories":["tool-use-integration","model-experimentation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_vibecode-mcp-prompt__cap_2","uri":"capability://memory.knowledge.contextual.prompt.storage","name":"contextual prompt storage","description":"This capability provides a mechanism for storing and retrieving contextual prompts based on user sessions. It leverages a lightweight database to maintain a history of prompts and their corresponding outputs, allowing users to revisit and refine previous prompts easily. The design ensures that context is preserved across sessions, making it easier to track changes and improvements over time.","intents":["How can I save my previous prompts for future reference?","I want to track the evolution of my prompts and their outputs over time.","Can I retrieve past prompts to refine them further?"],"best_for":["developers looking to maintain a history of prompt iterations"],"limitations":["Requires additional storage management and may increase complexity."],"requires":["Node.js 14+","Database setup for storing prompts"],"input_types":["text"],"output_types":["text"],"categories":["memory-knowledge","prompt-management"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":27,"verified":false,"data_access_risk":"high","permissions":["Node.js 14+","MCP-compatible language model API key","API keys for each integrated model","Database setup for storing prompts"],"failure_modes":["Requires continuous user input for effective refinement, which may not suit all use cases.","Performance may vary based on the number of models integrated and their individual latencies.","Requires additional storage management and may increase complexity.","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.16,"ecosystem":0.48999999999999994,"match_graph":0.25,"freshness":0.75,"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":"2026-05-03T15:19:22.208Z","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=vibecode-mcp-prompt","compare_url":"https://unfragile.ai/compare?artifact=vibecode-mcp-prompt"}},"signature":"BtRaUPOs8tpwUXqkkeE0gDH9X9MtXJfSF/n5Vplx6tZOKYhaREqbSN8rVudmcwUX5uKlmDOZMhz6FsK0cjNtBA==","signedAt":"2026-06-23T08:29:34.863Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/vibecode-mcp-prompt","artifact":"https://unfragile.ai/vibecode-mcp-prompt","verify":"https://unfragile.ai/api/v1/verify?slug=vibecode-mcp-prompt","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"}}