{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"smithery_bypawel-tachibot-mcp","slug":"bypawel-tachibot-mcp","name":"tachibot-mcp","type":"mcp","url":"https://github.com/byPawel/tachibot-mcp","page_url":"https://unfragile.ai/bypawel-tachibot-mcp","categories":["mcp-servers"],"tags":["mcp","model-context-protocol","smithery:byPawel/tachibot-mcp"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"smithery_bypawel-tachibot-mcp__cap_0","uri":"capability://safety.moderation.parallel.model.validation.and.error.detection","name":"parallel model validation and error detection","description":"This capability allows multiple AI models from different providers to run in parallel, where they evaluate each other's outputs. By implementing a debate mechanism, the system checks for inconsistencies and potential errors before presenting results to the user. This multi-model approach reduces the risk of hallucinations by leveraging diverse perspectives from models like OpenAI, Google, and Anthropic.","intents":["How can I ensure the accuracy of AI-generated responses?","What methods can I use to cross-verify outputs from different AI models?","How do I minimize hallucinations in AI outputs?"],"best_for":["developers building AI applications requiring high accuracy"],"limitations":["Increased computational overhead due to running multiple models simultaneously","Latency may increase with more models engaged in validation"],"requires":["API keys for OpenAI, Google, Anthropic, and other supported models","Python 3.8+"],"input_types":["text","structured prompts"],"output_types":["text","validated responses"],"categories":["safety-moderation","ai-tools"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_bypawel-tachibot-mcp__cap_1","uri":"capability://tool.use.integration.integrated.model.orchestration","name":"integrated model orchestration","description":"This capability orchestrates the interaction between various AI models through a unified interface, allowing for seamless switching and integration of different model outputs. By using a context-aware protocol, it ensures that the relevant context is maintained across model calls, enabling coherent and contextually appropriate responses.","intents":["How can I integrate multiple AI models into my application?","What is the best way to manage context across different AI services?","How do I switch between models based on specific tasks?"],"best_for":["teams developing complex AI systems with multiple model dependencies"],"limitations":["Requires careful management of context to avoid loss of coherence","May not support all model features uniformly"],"requires":["Node.js 14+","API keys for all integrated models"],"input_types":["text","contextual data"],"output_types":["text","model-specific outputs"],"categories":["tool-use-integration","mcp-servers"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_bypawel-tachibot-mcp__cap_2","uri":"capability://text.generation.language.dynamic.response.generation.based.on.model.consensus","name":"dynamic response generation based on model consensus","description":"This capability generates final outputs based on the consensus reached by multiple models, allowing for a more reliable response. It employs a voting mechanism where each model's output is weighted based on its historical accuracy, ensuring that the most reliable models have a greater influence on the final output.","intents":["How can I generate a final response that reflects the best insights from multiple models?","What techniques can I use to aggregate outputs from different AI sources?","How do I ensure the reliability of AI-generated content?"],"best_for":["content creators seeking high-quality AI-generated text"],"limitations":["Consensus may not always lead to the best output if all models are flawed","Requires extensive historical data to weight models accurately"],"requires":["Access to historical performance data of models","Python 3.9+"],"input_types":["text","model outputs"],"output_types":["text","aggregated responses"],"categories":["text-generation-language","ai-tools"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_bypawel-tachibot-mcp__cap_3","uri":"capability://safety.moderation.contextual.error.correction","name":"contextual error correction","description":"This capability allows the system to identify and correct errors in AI outputs based on contextual cues from the input. By analyzing the context in which a response is generated, it can apply specific correction algorithms that are tailored to the nuances of the content, improving overall accuracy.","intents":["How can I automatically correct inaccuracies in AI-generated text?","What methods can I use to enhance the quality of AI outputs?","How do I ensure that AI responses are contextually appropriate?"],"best_for":["developers focused on improving AI output quality"],"limitations":["Error correction algorithms may not cover all types of inaccuracies","Requires well-defined context to function effectively"],"requires":["Python 3.8+","Access to model training data"],"input_types":["text","contextual prompts"],"output_types":["text","corrected outputs"],"categories":["safety-moderation","ai-tools"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_bypawel-tachibot-mcp__cap_4","uri":"capability://planning.reasoning.multi.model.feedback.loop","name":"multi-model feedback loop","description":"This capability creates a feedback loop where outputs from one model can be used to refine the inputs for another, allowing for iterative improvement of responses. By establishing a continuous cycle of feedback, the system enhances the quality of outputs over time through adaptive learning.","intents":["How can I improve the quality of AI responses over time?","What strategies can I implement for iterative learning in AI systems?","How do I set up a feedback mechanism for AI outputs?"],"best_for":["AI researchers developing adaptive learning systems"],"limitations":["Requires significant computational resources for continuous learning","May introduce complexity in managing feedback loops"],"requires":["Access to a robust data storage solution","Python 3.9+"],"input_types":["text","model outputs"],"output_types":["text","refined outputs"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":30,"verified":false,"data_access_risk":"moderate","permissions":["API keys for OpenAI, Google, Anthropic, and other supported models","Python 3.8+","Node.js 14+","API keys for all integrated models","Access to historical performance data of models","Python 3.9+","Access to model training data","Access to a robust data storage solution"],"failure_modes":["Increased computational overhead due to running multiple models simultaneously","Latency may increase with more models engaged in validation","Requires careful management of context to avoid loss of coherence","May not support all model features uniformly","Consensus may not always lead to the best output if all models are flawed","Requires extensive historical data to weight models accurately","Error correction algorithms may not cover all types of inaccuracies","Requires well-defined context to function effectively","Requires significant computational resources for continuous learning","May introduce complexity in managing feedback loops","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.35,"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":"2026-05-24T12:16:25.636Z","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=bypawel-tachibot-mcp","compare_url":"https://unfragile.ai/compare?artifact=bypawel-tachibot-mcp"}},"signature":"RQq4sFJzHzr54YnsxruwBBRigbuFAvr/gG8bTVrbaWBeFoIeU+jbZTdnp5q2ooDijwp7fT3IEpYkRBvno1bvCA==","signedAt":"2026-07-09T07:37:58.503Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/bypawel-tachibot-mcp","artifact":"https://unfragile.ai/bypawel-tachibot-mcp","verify":"https://unfragile.ai/api/v1/verify?slug=bypawel-tachibot-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"}}