{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github_mcp-nanbingxyz-5ire","slug":"mcp-nanbingxyz-5ire","name":"5ire","type":"mcp","url":"https://github.com/nanbingxyz/5ire","page_url":"https://unfragile.ai/mcp-nanbingxyz-5ire","categories":["mcp-servers"],"tags":["knowledge-base","llms","mcp","mcp-client","model-context-protocol"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github_mcp-nanbingxyz-5ire__cap_0","uri":"capability://text.generation.language.multi.provider.ai.chat.with.unified.streaming.interface","name":"multi-provider ai chat with unified streaming interface","description":"Abstracts 12+ AI providers (OpenAI, Anthropic, Google, Mistral, Grok, DeepSeek, Ollama, Perplexity, Doubao, etc.) behind a single chat interface using a provider-agnostic ChatService base architecture with provider-specific implementations. Streams responses in real-time via Electron IPC bridge, manages per-conversation model selection and parameters, and handles token counting/cost estimation across heterogeneous provider APIs.","intents":["Switch between different AI providers mid-project without rewriting chat logic","Compare responses from multiple providers on the same prompt","Configure model-specific parameters (temperature, max_tokens) per conversation","Track API consumption and costs across different provider accounts"],"best_for":["developers building multi-model AI applications","teams evaluating different LLM providers","power users who want provider flexibility without vendor lock-in"],"limitations":["Provider-specific features (e.g., vision, function calling) require per-provider implementation; no automatic capability detection","Token counting varies by provider; estimates may not match actual billing","Streaming latency depends on provider response times; no client-side response caching"],"requires":["Electron 31.7.1+","API keys for at least one provider (OpenAI, Anthropic, etc.)","Node.js v20.10.0+"],"input_types":["text prompts","conversation history (JSON)","system messages"],"output_types":["streamed text responses","structured metadata (tokens, cost, provider)"],"categories":["text-generation-language","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-nanbingxyz-5ire__cap_1","uri":"capability://tool.use.integration.mcp.server.integration.with.multi.transport.support","name":"mcp server integration with multi-transport support","description":"Implements Model Context Protocol (MCP) client that connects to local and remote tool servers via three transport mechanisms: StdioTransport (local processes), SSETransport (HTTP Server-Sent Events), and StreamableHTTPTransport (streaming HTTP). Manages tool discovery, schema validation, and execution with user approval policies. Tools are executed in the main Electron process and results are injected into chat context for model reasoning.","intents":["Connect custom tool servers (file system, databases, APIs) to AI models for autonomous execution","Approve/deny tool calls before execution to maintain user control","Discover available tools from MCP servers and expose them to chat models","Chain multiple tool calls across different MCP servers in a single conversation"],"best_for":["developers building AI agents with external tool access","teams deploying custom MCP servers for domain-specific tasks","users who need fine-grained control over what tools AI can execute"],"limitations":["Tool execution is synchronous; long-running tools block the chat UI until completion","No built-in tool result caching; repeated tool calls re-execute","MCP server crashes are not automatically recovered; manual restart required","Tool approval UI is modal; cannot batch-approve multiple tool calls"],"requires":["MCP server running locally or accessible via HTTP","Tool schema must conform to MCP specification","Electron 31.7.1+"],"input_types":["MCP server configuration (host, port, transport type)","tool schemas (JSON)","tool call requests from models"],"output_types":["tool execution results (JSON)","tool availability metadata","approval/denial decisions"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-nanbingxyz-5ire__cap_10","uri":"capability://safety.moderation.tool.execution.approval.workflow.with.user.control","name":"tool execution approval workflow with user control","description":"Implements a modal approval UI that intercepts tool calls before execution. Users can review the tool name, parameters, and expected side effects before approving or denying. Approved tools are executed in the main Electron process with results injected back into the chat context. Supports approval policies (e.g., 'always approve file reads, always deny file writes') to reduce approval fatigue.","intents":["Prevent AI from executing dangerous tools (file deletion, API calls) without user consent","Review tool parameters before execution to catch mistakes","Set approval policies for common tools to reduce manual approvals","Audit which tools were executed and when"],"best_for":["teams deploying AI agents in production","users who need fine-grained control over AI actions","organizations with security/compliance requirements"],"limitations":["Approval UI is modal; blocks chat interaction until user responds","No batch approval for multiple tool calls; each tool requires separate approval","Approval policies are simple (allow/deny); no conditional policies based on parameters","No audit logging; approval history is not persisted"],"requires":["Electron 31.7.1+","React 18.3.1+ for modal UI","MCP server with tool schemas"],"input_types":["tool call request (name, parameters)","tool schema (JSON)"],"output_types":["approval decision (boolean)","tool execution result"],"categories":["safety-moderation","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-nanbingxyz-5ire__cap_11","uri":"capability://automation.workflow.state.management.with.zustand.and.electron.store.persistence","name":"state management with zustand and electron store persistence","description":"Uses Zustand for in-memory state management in the React renderer process (conversations, messages, UI state) and Electron Store for persistent state in the main process (provider configs, API keys, user preferences). State is synced between processes via IPC: renderer dispatches actions, main process updates persistent store, and updates are broadcast back to renderer. This separation ensures sensitive data (API keys) stays in the main process.","intents":["Maintain application state across app restarts","Synchronize state between multiple windows","Keep sensitive data (API keys) out of the renderer process","Enable undo/redo by maintaining state history"],"best_for":["Electron applications with complex state","teams requiring secure state management","applications with multi-window support"],"limitations":["IPC round-trips add latency; state updates are not instant","Zustand state is in-memory; large state objects (100k+ items) may cause memory issues","Electron Store uses synchronous file I/O; large state writes may block the main process","No built-in conflict resolution for concurrent state updates from multiple windows"],"requires":["Zustand for renderer state","Electron Store for main process state","Electron 31.7.1+"],"input_types":["state updates (actions)","IPC messages"],"output_types":["updated state (JSON)","persisted configuration"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-nanbingxyz-5ire__cap_2","uri":"capability://memory.knowledge.local.knowledge.base.with.vector.embeddings.and.rag","name":"local knowledge base with vector embeddings and rag","description":"Ingests documents (PDF, DOCX, XLSX, TXT) into a local SQLite + LanceDB vector store using bge-m3 embeddings generated locally via @xenova/transformers. Implements semantic search with citation tracking, allowing models to retrieve relevant document chunks and cite sources in responses. Knowledge base is persisted locally; optional Supabase sync enables cross-device access.","intents":["Upload company documents and have AI reference them in conversations","Search documents semantically without full-text indexing","Cite sources when AI references knowledge base content","Sync knowledge base across multiple devices via Supabase"],"best_for":["teams with proprietary documents (contracts, manuals, research)","users who need offline document search and AI reasoning","organizations requiring document provenance and citation"],"limitations":["Embedding generation is CPU-intensive; large document batches (100+ PDFs) may take minutes","bge-m3 embeddings are fixed-dimension; no fine-tuning for domain-specific vocabulary","LanceDB is embedded; no distributed vector database support for large-scale deployments","Citation accuracy depends on chunk boundaries; may cite irrelevant chunks if semantic similarity is high"],"requires":["Electron 31.7.1+","SQLite (better-sqlite3) for metadata storage","LanceDB for vector storage","@xenova/transformers for local embeddings","Minimum 2GB free disk space for vector index"],"input_types":["PDF files","DOCX files","XLSX files","TXT files","semantic search queries"],"output_types":["retrieved document chunks","citation metadata (document name, page, chunk ID)","relevance scores"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-nanbingxyz-5ire__cap_3","uri":"capability://tool.use.integration.dynamic.provider.configuration.and.api.key.management","name":"dynamic provider configuration and api key management","description":"Manages 12+ AI provider configurations with encrypted API key storage using Electron Store. Supports dynamic model discovery (fetching available models from provider APIs), custom provider registration with user-defined endpoints, and per-provider parameter validation. API keys are encrypted at rest and never exposed to the renderer process; all provider communication happens in the main Electron process.","intents":["Add new AI providers without modifying application code","Securely store and rotate API keys across multiple provider accounts","Discover new models when providers release updates","Configure custom endpoints for self-hosted or proxy providers (e.g., Ollama)"],"best_for":["teams managing multiple provider accounts","users who want to add custom LLM providers (local Ollama, proxy services)","organizations with security requirements for API key encryption"],"limitations":["Model discovery requires live API calls; offline mode cannot refresh model lists","Custom provider registration requires manual endpoint configuration; no auto-discovery","Encryption uses Electron Store's default cipher; not FIPS-compliant","No audit logging for API key access or provider configuration changes"],"requires":["Electron 31.7.1+","Electron Store for encrypted storage","Valid API key for at least one provider"],"input_types":["provider name (string)","API endpoint (URL)","API key (string)","model parameters (JSON)"],"output_types":["provider configuration object","available models list","validation errors"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-nanbingxyz-5ire__cap_4","uri":"capability://data.processing.analysis.token.counting.and.usage.analytics.across.providers","name":"token counting and usage analytics across providers","description":"Tracks API consumption per conversation and provider using provider-specific token counting logic. Estimates costs based on provider pricing models (input/output token rates). Aggregates usage metrics in SQLite for historical analysis. Supports both exact token counting (for OpenAI via tiktoken) and estimation (for providers without public token counting).","intents":["Monitor API spending across multiple provider accounts","Estimate conversation costs before sending requests","Identify expensive models or conversation patterns","Generate usage reports for billing or cost allocation"],"best_for":["teams with limited API budgets","developers optimizing for cost efficiency","organizations tracking per-project or per-user API spending"],"limitations":["Token counting is approximate for providers without public APIs; actual billing may differ","Cost estimates use static pricing; does not account for volume discounts or dynamic pricing","No real-time cost alerts; users must manually check usage dashboard","Historical data is stored locally; no cross-device usage sync without Supabase"],"requires":["Electron 31.7.1+","SQLite for usage storage","Provider API keys for accurate token counting"],"input_types":["conversation messages (text)","model name (string)","provider name (string)"],"output_types":["token count (integer)","estimated cost (float)","usage metrics (JSON)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-nanbingxyz-5ire__cap_5","uri":"capability://automation.workflow.conversation.management.with.multi.model.comparison","name":"conversation management with multi-model comparison","description":"Organizes conversations in a hierarchical structure (folders, tags) with SQLite persistence. Supports per-conversation model and provider selection, allowing users to compare responses from different models on the same prompt. Implements conversation forking (branching from a specific message) and message editing with automatic re-generation. Conversation state is managed via Zustand in the renderer process and synced to SQLite in the main process.","intents":["Organize conversations by project, topic, or date","Compare how different models respond to the same prompt","Branch conversations to explore alternative reasoning paths","Edit previous messages and regenerate responses"],"best_for":["researchers comparing model capabilities","developers iterating on prompts","teams collaborating on AI-assisted tasks"],"limitations":["Conversation branching creates separate conversation trees; no merge functionality","Message editing re-generates only the edited message and its descendants; does not update sibling branches","No built-in conversation sharing; users must export conversations manually","Zustand state is in-memory; large conversations (1000+ messages) may cause UI lag"],"requires":["Electron 31.7.1+","React 18.3.1+","Zustand for state management","SQLite for persistence"],"input_types":["conversation metadata (title, tags, folder)","messages (text, role, model)","model/provider selection"],"output_types":["conversation tree (JSON)","message history","comparison results"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-nanbingxyz-5ire__cap_6","uri":"capability://automation.workflow.cross.platform.desktop.application.with.electron.ipc.security","name":"cross-platform desktop application with electron ipc security","description":"Built on Electron 31.7.1 with a three-process architecture: Main Process (Node.js, handles API calls and file I/O), Renderer Process (React 18.3.1, UI), and Preload Script (sandboxed context bridge). Uses contextBridge to expose only whitelisted IPC methods to the renderer, preventing direct access to Node.js APIs. Supports Windows, macOS, and Linux with native OS integrations.","intents":["Run AI assistant as a native desktop application with system tray integration","Access local file system for knowledge base uploads and document processing","Maintain API keys and sensitive data in the secure main process","Distribute application across Windows, macOS, and Linux with single codebase"],"best_for":["teams building desktop-first AI tools","users who need offline-capable AI assistants","organizations with security requirements for local data storage"],"limitations":["Electron apps have larger memory footprint (~200-300MB) than web apps","IPC overhead adds ~10-50ms latency per round-trip; not suitable for real-time applications","contextBridge security model requires careful API design; misconfiguration can expose Node.js APIs","Cross-platform testing requires building and testing on Windows, macOS, and Linux"],"requires":["Electron 31.7.1+","Node.js v20.10.0+","React 18.3.1+","Fluent UI for native-looking components"],"input_types":["user interactions (clicks, text input)","file system paths","IPC messages"],"output_types":["rendered UI (React components)","file system operations","system notifications"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-nanbingxyz-5ire__cap_7","uri":"capability://automation.workflow.localization.system.with.multi.language.ui","name":"localization system with multi-language ui","description":"Implements i18n (internationalization) using JSON translation files stored in `public/locales/{language}/translation.json`. Supports English and Chinese (Simplified) with extensible architecture for additional languages. Translations are loaded at startup and injected into React components via context or hooks. UI components from Fluent UI automatically adapt to language selection.","intents":["Provide UI in multiple languages without code changes","Add new languages by contributing translation files","Ensure consistent terminology across the application"],"best_for":["teams building international applications","open-source projects accepting community translations","organizations supporting multiple regional markets"],"limitations":["Translation files are static; no dynamic translation updates without app restart","No pluralization or gender-aware translation rules; requires manual handling","Missing translations fall back to English; no warning for incomplete translations","RTL languages (Arabic, Hebrew) not supported by current UI framework"],"requires":["JSON translation files in `public/locales/{language}/translation.json`","React 18.3.1+ for context/hooks","i18n library (e.g., i18next)"],"input_types":["translation keys (strings)","language code (e.g., 'en', 'zh')"],"output_types":["translated UI text","localized date/time formats"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-nanbingxyz-5ire__cap_8","uri":"capability://text.generation.language.message.rendering.with.markdown.and.code.syntax.highlighting","name":"message rendering with markdown and code syntax highlighting","description":"Renders chat messages as markdown with syntax-highlighted code blocks. Uses a markdown parser to convert message text to React components, with custom renderers for code blocks (using Prism.js or similar), links, and images. Supports inline LaTeX math rendering. Messages are streamed character-by-character for real-time display, with a streaming indicator showing when the model is still generating.","intents":["Display AI responses with proper formatting (bold, italics, lists, code blocks)","Show code with syntax highlighting for multiple programming languages","Render mathematical equations in conversations","Display streaming responses in real-time as they arrive from the model"],"best_for":["developers who need code examples in AI responses","researchers discussing mathematical concepts","users who want rich-text chat experience"],"limitations":["Markdown parsing adds ~50-100ms overhead per message; large messages may have noticeable delay","Code syntax highlighting is limited to languages supported by Prism.js; custom languages require configuration","LaTeX rendering is client-side; complex equations may render slowly","Streaming character-by-character can cause UI jank if message is very long (1000+ characters)"],"requires":["React 18.3.1+","markdown parser library (e.g., react-markdown)","Prism.js for syntax highlighting","KaTeX or MathJax for LaTeX rendering"],"input_types":["markdown text","code blocks with language tags","LaTeX equations"],"output_types":["rendered React components","HTML with syntax highlighting","styled code blocks"],"categories":["text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-nanbingxyz-5ire__cap_9","uri":"capability://text.generation.language.chat.editor.with.model.parameter.controls","name":"chat editor with model parameter controls","description":"Provides a rich text editor for composing messages with support for multi-line input, markdown preview, and message history (up/down arrow navigation). Exposes model-specific parameters (temperature, top_p, max_tokens, etc.) as UI controls that vary by provider. Parameters are validated against provider constraints before sending requests. System message editing is supported per-conversation.","intents":["Compose multi-line prompts with markdown formatting","Adjust model parameters (temperature, max_tokens) for different use cases","Set system messages to guide model behavior","Navigate message history without re-typing"],"best_for":["power users who need fine-grained control over model behavior","researchers experimenting with different parameter settings","developers optimizing prompts for specific tasks"],"limitations":["Parameter validation is provider-specific; invalid parameters are caught at request time, not in the UI","No parameter presets or templates; users must manually adjust parameters each conversation","Message history is limited to current session; no persistent history across app restarts","No parameter suggestions based on conversation context"],"requires":["React 18.3.1+","provider configuration with parameter schemas"],"input_types":["text input (markdown)","parameter values (float, integer)","system message (text)"],"output_types":["formatted message (markdown)","parameter object (JSON)","request payload"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":48,"verified":false,"data_access_risk":"high","permissions":["Electron 31.7.1+","API keys for at least one provider (OpenAI, Anthropic, etc.)","Node.js v20.10.0+","MCP server running locally or accessible via HTTP","Tool schema must conform to MCP specification","React 18.3.1+ for modal UI","MCP server with tool schemas","Zustand for renderer state","Electron Store for main process state","SQLite (better-sqlite3) for metadata storage"],"failure_modes":["Provider-specific features (e.g., vision, function calling) require per-provider implementation; no automatic capability detection","Token counting varies by provider; estimates may not match actual billing","Streaming latency depends on provider response times; no client-side response caching","Tool execution is synchronous; long-running tools block the chat UI until completion","No built-in tool result caching; repeated tool calls re-execute","MCP server crashes are not automatically recovered; manual restart required","Tool approval UI is modal; cannot batch-approve multiple tool calls","Approval UI is modal; blocks chat interaction until user responds","No batch approval for multiple tool calls; each tool requires separate approval","Approval policies are simple (allow/deny); no conditional policies based on parameters","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.5851420736903379,"quality":0.49,"ecosystem":0.55,"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":"2026-05-24T12:16:22.065Z","last_scraped_at":"2026-05-03T14:23:31.492Z","last_commit":"2026-03-18T09:40:08Z"},"community":{"stars":5192,"forks":406,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=mcp-nanbingxyz-5ire","compare_url":"https://unfragile.ai/compare?artifact=mcp-nanbingxyz-5ire"}},"signature":"EJwxMSD9l/5DcJfEgQpW6KxZKh29+u7QiP/YnRPgxfP8zHEJGBuCD6QhSaK2xawpqOXW2XEFzVd0C3aEvnSaDw==","signedAt":"2026-06-20T11:09:20.738Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/mcp-nanbingxyz-5ire","artifact":"https://unfragile.ai/mcp-nanbingxyz-5ire","verify":"https://unfragile.ai/api/v1/verify?slug=mcp-nanbingxyz-5ire","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"}}