{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"tool_magai","slug":"magai","name":"Magai","type":"product","url":"https://magai.co","page_url":"https://unfragile.ai/magai","categories":["chatbots-assistants"],"tags":[],"pricing":{"model":"freemium","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"tool_magai__cap_0","uri":"capability://tool.use.integration.multi.model.parallel.query.execution","name":"multi-model parallel query execution","description":"Sends a single user prompt simultaneously to multiple AI APIs (ChatGPT, Claude, Bard, etc.) and aggregates responses in a unified interface. Magai maintains separate API connections to each provider's endpoint, handles authentication via user-supplied API keys, and orchestrates concurrent requests to minimize latency while collecting all responses for side-by-side comparison.","intents":["Compare how different AI models interpret and respond to the same prompt without manual tab-switching","Identify which AI model produces the most accurate or useful response for a specific task","Evaluate model strengths and weaknesses across reasoning, creativity, and factual accuracy in real-time","Reduce time spent testing the same query across multiple AI services by 80%"],"best_for":["Researchers and data scientists validating AI model behavior across providers","Content creators and copywriters A/B testing tone and quality across models","Power users who need the 'best' answer from a pool of AI services"],"limitations":["Requires users to provision and manage separate API keys for each AI service, creating authentication overhead","Response latency is bounded by the slowest API provider in the parallel request set (no timeout-based fallback documented)","UI becomes cognitively overloaded when comparing 4+ model outputs simultaneously; no built-in response filtering or ranking","No streaming response support documented; full responses must complete before display, increasing perceived latency"],"requires":["Valid API keys for each AI service (OpenAI, Anthropic, Google, etc.)","Active internet connection with sufficient bandwidth for concurrent API calls","Web browser with modern JavaScript support (ES6+)"],"input_types":["text (plain prompts, questions, instructions)"],"output_types":["text (model responses)","structured metadata (model name, response time, token count)"],"categories":["tool-use-integration","multi-provider-orchestration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_magai__cap_1","uri":"capability://memory.knowledge.prompt.template.library.and.reuse.system","name":"prompt template library and reuse system","description":"Stores, organizes, and retrieves user-created prompt templates with variable substitution and tagging. Templates are persisted in user account storage (likely cloud-backed), support parameterization via placeholder syntax (e.g., {{variable}}), and enable one-click execution across all connected AI models with consistent formatting and context injection.","intents":["Save frequently-used prompts (e.g., 'summarize this article', 'generate code review comments') to avoid retyping","Create parameterized templates that accept dynamic inputs (document title, code snippet, topic) without manual editing","Maintain a personal library of domain-specific prompts for consistent quality across repeated tasks","Share prompt templates with team members or reuse across different AI models"],"best_for":["Content creators running similar queries repeatedly (e.g., 'summarize in 100 words')","Developers building prompt-driven workflows who need version control for prompts","Teams standardizing on prompt formats for consistency and compliance"],"limitations":["No version control or rollback mechanism for template changes; overwrites are destructive","Template variables are simple string substitution; no conditional logic, loops, or complex templating syntax","No built-in sharing or permission model; templates are user-scoped, not team-scoped","No analytics on template usage or effectiveness; users cannot track which templates produce best results"],"requires":["Magai account with cloud storage enabled","Basic understanding of placeholder syntax ({{variable}})"],"input_types":["text (template definition with placeholders)","text (variable values at execution time)"],"output_types":["text (rendered prompt sent to AI models)","structured metadata (template name, creation date, usage count)"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_magai__cap_10","uri":"capability://automation.workflow.freemium.tier.with.usage.limits","name":"freemium tier with usage limits","description":"Provides a free tier with limited API query allowances (likely 5-10 queries per day or per month) and premium features gated behind a subscription. Free tier includes core functionality (multi-model comparison, conversation history, templates) but with reduced query limits and no advanced features (bulk export, advanced analytics, team sharing). Limits are enforced server-side and reset on a daily or monthly cadence.","intents":["Try Magai's core value proposition without upfront payment","Evaluate whether multi-model comparison is useful before committing to a subscription","Use Magai for occasional queries without paying for premium features","Understand the pricing model and feature tiers before upgrading"],"best_for":["Individual users evaluating Magai for personal use","Researchers testing multi-model comparison workflows","Users with low query volume who don't need premium features"],"limitations":["Query limits are restrictive; power users will quickly exhaust free tier allowances","No clear documentation of what features are gated behind premium tier","Free tier may have degraded performance or longer response times","Upgrade path is not seamless; users may lose free tier data or settings when upgrading"],"requires":["Magai account (free signup)"],"input_types":["text (user prompt)"],"output_types":["text (model responses)"],"categories":["automation-workflow","business-model"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_magai__cap_2","uri":"capability://memory.knowledge.conversation.history.and.context.management","name":"conversation history and context management","description":"Maintains persistent conversation threads across multiple AI models, storing message history, metadata (timestamps, model used, token counts), and enabling retrieval of past exchanges. Conversations are indexed by user account and searchable, allowing users to resume multi-turn dialogues with context preservation across sessions without re-prompting.","intents":["Resume a conversation with an AI model days or weeks later without losing context or starting over","Search across all past conversations to find a previous answer or insight","Compare how different models responded to the same multi-turn conversation","Maintain separate conversation threads for different projects or topics without mixing context"],"best_for":["Researchers conducting iterative investigations across multiple sessions","Content creators building on previous brainstorming sessions","Users who need audit trails or documentation of AI-assisted decision-making"],"limitations":["Context window is bounded by each AI model's token limit; long conversations may require manual summarization or truncation","No automatic context compression or summarization; users must manually manage conversation length","Search is likely keyword-based; no semantic search across conversation content (no embedding-based retrieval documented)","No conversation branching or versioning; users cannot explore alternative conversation paths from a single point"],"requires":["Magai account with cloud storage","Active internet connection for history sync"],"input_types":["text (user messages, AI responses)"],"output_types":["text (conversation history with metadata)","structured data (conversation metadata: date, model, token count)"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_magai__cap_3","uri":"capability://text.generation.language.unified.chat.interface.with.side.by.side.response.rendering","name":"unified chat interface with side-by-side response rendering","description":"Renders responses from multiple AI models in a single viewport using a multi-column or tabbed layout, allowing users to read and compare outputs without switching windows or tabs. The interface handles variable response lengths, formatting preservation (code blocks, lists, etc.), and provides UI controls for copying, editing, or re-running queries against individual models.","intents":["Compare model outputs visually without cognitive overhead of tab-switching","Identify formatting or structural differences in how models present the same information","Copy the best response or merge insights from multiple models into a final answer","Quickly re-run a query against a single model if one response is unsatisfactory"],"best_for":["Power users comparing 2-3 models simultaneously","Researchers documenting model behavior differences","Users with large monitors who can afford horizontal screen real estate"],"limitations":["UI becomes cluttered and difficult to parse when comparing 4+ models; no automatic layout optimization for many models","Responsive design challenges on smaller screens (tablets, laptops); side-by-side layout breaks on <1200px width","No built-in response filtering, ranking, or sorting; users must manually evaluate which response is 'best'","Copy-paste workflow is manual; no one-click merge or synthesis of multiple responses into a single output"],"requires":["Web browser with CSS Grid or Flexbox support","Minimum screen width of ~1200px for comfortable 2-model comparison"],"input_types":["text (user prompt)"],"output_types":["rendered HTML (formatted responses with syntax highlighting, lists, etc.)"],"categories":["text-generation-language","user-interface"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_magai__cap_4","uri":"capability://tool.use.integration.multi.provider.api.key.management.and.authentication","name":"multi-provider api key management and authentication","description":"Provides a secure credential storage and management system for API keys from multiple AI providers (OpenAI, Anthropic, Google, etc.). Keys are encrypted at rest, scoped to the user account, and injected into API requests at runtime without exposing them to the client-side application. Supports key rotation, revocation, and per-provider rate limiting configuration.","intents":["Store API keys for multiple AI services in one place without hardcoding them in scripts or environment files","Rotate or revoke API keys without disrupting active conversations","Prevent accidental exposure of API keys in browser DevTools or network logs","Configure per-provider rate limits or usage quotas to control costs"],"best_for":["Users managing credentials for 3+ AI services","Teams sharing a Magai instance who need isolated credential scopes","Users concerned about API key security and compliance"],"limitations":["Users must manually provision API keys from each provider; no OAuth or federated identity support documented","No audit logging of API key usage or access; users cannot track which keys were used for which requests","Credential storage is Magai-managed; users must trust Magai's encryption and security practices","No support for temporary or time-limited credentials (e.g., STS tokens); only static API keys"],"requires":["Valid API keys from each AI provider (OpenAI, Anthropic, Google, etc.)","Magai account with credential storage enabled"],"input_types":["text (API key strings)"],"output_types":["structured metadata (provider name, key status, last used date)"],"categories":["tool-use-integration","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_magai__cap_5","uri":"capability://data.processing.analysis.response.quality.metrics.and.metadata.extraction","name":"response quality metrics and metadata extraction","description":"Automatically extracts and displays metadata about each AI response, including token count, generation time, model version, and estimated cost. Provides basic quality signals (e.g., response length, presence of code blocks) to help users evaluate response utility without manual inspection. Metrics are computed server-side and cached for performance.","intents":["Understand the computational cost of each API call to manage spending across multiple providers","Identify which model is fastest or most efficient for a given task","Track token usage over time to optimize prompt engineering","Compare response 'depth' (token count, structure) across models to assess effort"],"best_for":["Users on metered API plans who need cost visibility","Researchers benchmarking model performance (speed, efficiency)","Teams optimizing prompt engineering for cost-effectiveness"],"limitations":["Metrics are basic (token count, time); no semantic quality scoring or relevance ranking","Cost estimation is approximate and may not reflect actual billing due to provider-specific pricing tiers","No historical trend analysis or aggregation; users cannot see cost/performance trends over time","Metadata extraction is model-agnostic; no model-specific insights (e.g., confidence scores, reasoning traces)"],"requires":["Active API keys with usage tracking enabled"],"input_types":["text (user prompt)"],"output_types":["structured metadata (token count, generation time, estimated cost, model version)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_magai__cap_6","uri":"capability://text.generation.language.prompt.editing.and.re.execution.with.model.selection","name":"prompt editing and re-execution with model selection","description":"Allows users to edit a previously-submitted prompt and re-execute it against selected AI models without losing conversation context. Edited prompts are tracked with version history, and users can compare responses from the original and edited prompts side-by-side. Re-execution targets specific models (e.g., 'run against Claude only') or all connected models.","intents":["Refine a prompt based on initial responses and re-run it to get better results","Test prompt variations (e.g., adding 'be concise' or 'use bullet points') against multiple models","Compare how a model responds to slightly different phrasings of the same question","Iterate on a prompt without losing the original conversation thread"],"best_for":["Prompt engineers optimizing queries for quality and consistency","Researchers testing prompt sensitivity across model variants","Users iterating on complex queries that require multiple refinement cycles"],"limitations":["Version history is linear; no branching or comparison of multiple prompt variants from a single base","Re-execution does not preserve multi-turn context; edited prompts are treated as new queries","No A/B testing framework; users must manually compare results from different prompt versions","Editing is manual; no built-in prompt optimization suggestions or automated refinement"],"requires":["Active conversation with at least one AI model"],"input_types":["text (edited prompt)"],"output_types":["text (new model responses)","structured metadata (version history, comparison data)"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_magai__cap_7","uri":"capability://automation.workflow.response.copying.and.export.functionality","name":"response copying and export functionality","description":"Provides one-click copying of individual model responses or bulk export of entire conversations to clipboard, files, or external formats (Markdown, PDF, JSON). Supports selective export (e.g., 'export only Claude responses') and preserves formatting, metadata, and conversation structure in exported output.","intents":["Copy a model response to use in a document, email, or code editor","Export a full conversation for documentation, archival, or sharing with team members","Save conversations in portable formats (Markdown, PDF) for offline access","Extract structured data from conversations (e.g., JSON with responses and metadata) for analysis"],"best_for":["Content creators who need to move AI-generated text into publishing workflows","Researchers documenting AI model behavior for papers or reports","Teams sharing conversation insights across members"],"limitations":["Export formats are limited (Markdown, PDF, JSON); no support for Word, Google Docs, or other proprietary formats","PDF export may lose formatting or interactive elements (e.g., code syntax highlighting)","No built-in redaction or filtering; exported conversations include all metadata and model names","Bulk export is file-based; no direct integration with cloud storage (Google Drive, Dropbox, etc.)"],"requires":["Active conversation with at least one response"],"input_types":["text (conversation data)"],"output_types":["text (clipboard)","file (Markdown, PDF, JSON)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_magai__cap_8","uri":"capability://tool.use.integration.model.selection.and.switching","name":"model selection and switching","description":"Provides UI controls to select which AI models to query for a given prompt, enable/disable specific models mid-conversation, and switch between models for follow-up questions. Model selection is persistent per conversation but can be changed at any point. Supports dynamic model availability (e.g., disabling a model if API key is invalid or quota exceeded).","intents":["Choose which models to query for a specific prompt (e.g., 'only ask Claude and Bard')","Disable a model temporarily if its API is down or quota is exceeded","Switch to a different model for a follow-up question without starting a new conversation","Test a new model without affecting existing conversations"],"best_for":["Users who don't always need all models (e.g., 'Claude is best for this task')","Cost-conscious users who want to query only cheaper models for certain tasks","Users managing API quotas across multiple providers"],"limitations":["Model selection is per-conversation; no global defaults or per-task model preferences","No intelligent model recommendation; users must manually choose which models to use","Switching models mid-conversation may break context if the new model has different context window limits","No fallback mechanism; if a selected model fails, the query fails entirely (no automatic retry with alternative model)"],"requires":["At least one valid API key for an AI provider"],"input_types":["UI selection (model checkboxes or toggles)"],"output_types":["structured metadata (selected models, availability status)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_magai__cap_9","uri":"capability://memory.knowledge.conversation.organization.and.tagging","name":"conversation organization and tagging","description":"Allows users to organize conversations into folders or collections, apply custom tags or labels, and search/filter conversations by metadata. Conversations are indexed by user account and support full-text search across conversation content. Tags are user-defined and can be applied retroactively to existing conversations.","intents":["Organize conversations by project, topic, or date to avoid losing important exchanges","Tag conversations with custom labels (e.g., 'research', 'client-work', 'brainstorm') for quick retrieval","Search across all conversations to find a previous answer or insight","Archive or delete conversations to keep the interface clean"],"best_for":["Power users with hundreds of conversations who need organization","Teams sharing a Magai instance who need to categorize conversations by project","Researchers maintaining conversation archives for reproducibility"],"limitations":["Search is likely keyword-based; no semantic search or similarity-based retrieval","No shared folders or team-level organization; conversations are user-scoped","Tagging is manual; no automatic categorization or suggestion based on content","No bulk operations; users must tag conversations individually"],"requires":["Magai account with cloud storage"],"input_types":["text (conversation content, tags)"],"output_types":["structured metadata (conversation list, tags, search results)"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":40,"verified":false,"data_access_risk":"high","permissions":["Valid API keys for each AI service (OpenAI, Anthropic, Google, etc.)","Active internet connection with sufficient bandwidth for concurrent API calls","Web browser with modern JavaScript support (ES6+)","Magai account with cloud storage enabled","Basic understanding of placeholder syntax ({{variable}})","Magai account (free signup)","Magai account with cloud storage","Active internet connection for history sync","Web browser with CSS Grid or Flexbox support","Minimum screen width of ~1200px for comfortable 2-model comparison"],"failure_modes":["Requires users to provision and manage separate API keys for each AI service, creating authentication overhead","Response latency is bounded by the slowest API provider in the parallel request set (no timeout-based fallback documented)","UI becomes cognitively overloaded when comparing 4+ model outputs simultaneously; no built-in response filtering or ranking","No streaming response support documented; full responses must complete before display, increasing perceived latency","No version control or rollback mechanism for template changes; overwrites are destructive","Template variables are simple string substitution; no conditional logic, loops, or complex templating syntax","No built-in sharing or permission model; templates are user-scoped, not team-scoped","No analytics on template usage or effectiveness; users cannot track which templates produce best results","Query limits are restrictive; power users will quickly exhaust free tier allowances","No clear documentation of what features are gated behind premium tier","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.31666666666666665,"quality":0.72,"ecosystem":0.15000000000000002,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.1,"match_graph":0.35,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:31.857Z","last_scraped_at":"2026-04-05T13:23:42.560Z","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=magai","compare_url":"https://unfragile.ai/compare?artifact=magai"}},"signature":"Vjx6lQxfqiVg6JtwsAHK7yTNK+PuYPW/6IqP9c15Ra0OinkM2imAoaPGILXt4OByg17+GfNmMVPP8wp8+EDGCw==","signedAt":"2026-06-21T06:25:42.947Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/magai","artifact":"https://unfragile.ai/magai","verify":"https://unfragile.ai/api/v1/verify?slug=magai","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"}}