{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"vscode-warm3snow-vscode-ollama","slug":"vscode-ollama","name":"VSCode Ollama","type":"extension","url":"https://marketplace.visualstudio.com/items?itemName=warm3snow.vscode-ollama","page_url":"https://unfragile.ai/vscode-ollama","categories":["code-editors"],"tags":["ai","chat","llm","local","ollama"],"pricing":{"model":"freemium","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"vscode-warm3snow-vscode-ollama__cap_0","uri":"capability://text.generation.language.local.llm.chat.interface.with.streaming","name":"local-llm-chat-interface-with-streaming","description":"Provides a dedicated VS Code sidebar panel for conversational interaction with locally-running Ollama LLM instances via HTTP/REST API calls. Implements streaming response rendering to display model output token-by-token as it generates, reducing perceived latency. Maintains conversation history within the session, allowing multi-turn dialogue without re-sending full context each turn. Supports runtime model switching via UI dropdown without restarting the extension.","intents":["I want to ask coding questions without leaving VS Code or switching to a web browser","I need fast LLM responses without cloud API latency or privacy concerns","I want to iterate on code explanations and debugging within my editor","I need to switch between different local models (e.g., Mistral, Llama) mid-conversation"],"best_for":["solo developers building locally-first workflows","teams with privacy requirements preventing cloud API usage","developers with sufficient local GPU/CPU to run Ollama models","users already invested in the Ollama ecosystem"],"limitations":["Chat-only interface — no inline code completion or editor augmentation","Conversation history not persisted across VS Code sessions; lost on extension reload","Context window limited by selected model's architecture; no automatic context pruning or summarization","Streaming latency depends entirely on local hardware; no optimization for slow machines","No conversation export or sharing functionality documented"],"requires":["Visual Studio Code (minimum version unknown, likely 1.60+)","Ollama installed and running locally (https://ollama.ai)","At least one LLM model downloaded via Ollama (e.g., ollama pull mistral)","Ollama server accessible at default localhost:11434 or custom configured address"],"input_types":["plain text (user messages)","multi-line text (Shift+Enter for newlines)"],"output_types":["streamed plain text (model responses)","conversation history (in-memory, session-scoped)"],"categories":["text-generation-language","developer-tools"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-warm3snow-vscode-ollama__cap_1","uri":"capability://search.retrieval.web.search.integration.with.synthesis","name":"web-search-integration-with-synthesis","description":"Augments chat responses with real-time web search results by querying external sources and synthesizing findings into LLM responses. The extension fetches search results (implementation method unknown — likely via a search API or web scraping) and injects them as context into the LLM prompt, allowing the model to cite and reference current information. Results are displayed with citations, enabling users to verify claims and access sources.","intents":["I want to ask questions about current events, recent library versions, or breaking changes without relying on the model's training data cutoff","I need the LLM to cite sources so I can verify answers and access original documentation","I want to research API changes or deprecations that occurred after the model's training date"],"best_for":["developers needing up-to-date information on rapidly-evolving frameworks","teams researching recent security vulnerabilities or patches","users building knowledge-intensive applications requiring current data"],"limitations":["Web search requires active internet connectivity; fails silently or degrades gracefully if offline (behavior unknown)","Search result quality and citation accuracy depend on external search source; no control over result ranking or filtering","Search latency adds overhead to response time; no documented timeout or fallback if search is slow","No configuration for search scope, result count, or source filtering documented","Search API/source not documented; unclear if rate-limited or if extension hits API quotas"],"requires":["Active internet connection","Ollama server with web search capability enabled (configuration method unknown)"],"input_types":["plain text (user query)"],"output_types":["synthesized text with embedded citations","source links/references"],"categories":["search-retrieval","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-warm3snow-vscode-ollama__cap_10","uri":"capability://automation.workflow.keybinding.support.for.chat.input","name":"keybinding-support-for-chat-input","description":"Provides configurable keybindings for chat input operations: Enter sends the message, and Shift+Enter inserts a newline without sending. Keybindings follow VS Code's standard conventions and can be customized via keybindings.json. Enables efficient chat interaction without mouse clicks.","intents":["I want to send messages quickly without reaching for the mouse","I need to write multi-line prompts without accidentally sending incomplete messages","I want to customize keybindings to match my muscle memory from other tools"],"best_for":["keyboard-driven developers","users with custom keybinding preferences","teams standardizing on specific input conventions"],"limitations":["Only two keybindings documented (Enter, Shift+Enter); no other input shortcuts","Keybindings are not context-aware; Enter always sends, even if text is incomplete","No undo/redo for sent messages","No message editing after sending documented"],"requires":["VS Code keybindings.json configuration (optional; defaults provided)"],"input_types":["keyboard input (text and keybindings)"],"output_types":["message sent to Ollama API"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-warm3snow-vscode-ollama__cap_2","uri":"capability://planning.reasoning.thought.process.visualization","name":"thought-process-visualization","description":"Displays the LLM's intermediate reasoning steps or chain-of-thought process during response generation, allowing developers to inspect how the model arrived at its answer. Implementation details are undocumented, but likely involves parsing structured output from the LLM (e.g., XML tags, JSON reasoning blocks) or using Ollama's native reasoning APIs if available. Helps with debugging model behavior and understanding confidence levels.","intents":["I want to understand why the LLM gave a particular answer or recommendation","I need to debug incorrect model outputs by inspecting intermediate reasoning","I want to verify the model is using sound logic before trusting its code suggestions"],"best_for":["developers building AI-assisted tools who need interpretability","teams evaluating LLM reliability for critical tasks","researchers studying local model behavior"],"limitations":["Feature mentioned in documentation but implementation details completely undocumented","Unclear which models support thought-process output; likely requires models fine-tuned for reasoning (e.g., OpenAI o1 equivalent)","No control over verbosity or detail level of reasoning steps","Reasoning output format unknown; may be model-specific and inconsistent across different Ollama models","No documented way to disable or customize visualization"],"requires":["Ollama model with built-in reasoning/chain-of-thought support (specific models unknown)"],"input_types":["plain text (user query)"],"output_types":["structured reasoning steps (format unknown)","final response text"],"categories":["planning-reasoning","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-warm3snow-vscode-ollama__cap_3","uri":"capability://tool.use.integration.multi.model.runtime.switching","name":"multi-model-runtime-switching","description":"Allows users to switch between different LLM models at runtime via a UI dropdown selector without restarting the extension or losing conversation context. The extension queries the Ollama server for available models (via Ollama's list models API endpoint) and dynamically populates the selector. Switching models applies to subsequent messages in the conversation; prior messages retain their original model attribution (behavior inferred).","intents":["I want to compare how different models answer the same question without restarting the chat","I need to switch from a fast model to a more capable one for complex reasoning tasks","I want to test a newly-downloaded Ollama model without reloading VS Code"],"best_for":["developers experimenting with multiple local models","teams evaluating model performance for specific tasks","users with sufficient hardware to run multiple models"],"limitations":["Model switching only affects new messages; prior conversation context is not re-processed with the new model","No automatic model recommendation based on query complexity or task type","Model list is static at extension startup; newly-downloaded models require extension reload to appear","No model metadata displayed (parameters, size, capabilities) to help selection","Switching to a model not yet downloaded will fail silently or with unclear error (behavior unknown)"],"requires":["Multiple LLM models downloaded via Ollama (e.g., ollama pull mistral && ollama pull neural-chat)","Ollama server running and accessible"],"input_types":["UI dropdown selection"],"output_types":["model identifier (used for subsequent API calls)"],"categories":["tool-use-integration","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-warm3snow-vscode-ollama__cap_4","uri":"capability://tool.use.integration.configurable.ollama.server.connection","name":"configurable-ollama-server-connection","description":"Allows users to specify a custom Ollama server address (hostname and port) via VS Code settings, enabling connection to Ollama instances running on remote machines, Docker containers, or non-default ports. Configuration is stored in VS Code's settings.json and applied at extension initialization. Supports both localhost and network-accessible Ollama servers via HTTP/REST API.","intents":["I want to connect to Ollama running on a remote GPU server or Docker container","I need to run Ollama on a non-standard port and configure the extension to use it","I want to share a single Ollama instance across multiple developers via network access"],"best_for":["teams with centralized GPU infrastructure running Ollama","developers using Docker or Kubernetes for Ollama deployment","users with multiple machines who want to offload inference to a dedicated server"],"limitations":["No authentication/API key support documented; assumes Ollama server is on a trusted network","Network latency will significantly impact response times compared to localhost execution","No connection health check or retry logic documented; connection failures may hang the extension","Configuration requires manual editing of settings.json or VS Code settings UI; no in-extension setup wizard","No support for HTTPS or TLS encryption documented; assumes HTTP-only communication"],"requires":["Ollama server running and network-accessible at specified address","Network connectivity between VS Code machine and Ollama server","VS Code settings configuration (manual or via settings.json)"],"input_types":["server address string (e.g., 'localhost:11434' or '192.168.1.100:11434')"],"output_types":["HTTP/REST API connection to Ollama"],"categories":["tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-warm3snow-vscode-ollama__cap_5","uri":"capability://tool.use.integration.default.model.configuration","name":"default-model-configuration","description":"Allows users to specify a default LLM model via VS Code settings, which is automatically selected when the extension starts or when no model is explicitly chosen. Configuration is stored in VS Code's settings.json and applied at extension initialization. Reduces friction by eliminating the need to manually select a model for each chat session.","intents":["I want my preferred model to load automatically when I open VS Code","I want to set different default models for different VS Code workspaces","I want to avoid accidentally using a slow model when I forget to switch"],"best_for":["developers with a clear preference for one model","teams standardizing on a specific model for consistency","users with limited hardware who want to default to a lightweight model"],"limitations":["Only one default model per VS Code instance; no per-workspace or per-project model selection","No validation that the specified model is actually downloaded; will fail silently if model does not exist","Default model is global; no context-aware switching based on file type or project","Configuration requires manual settings.json editing; no UI wizard to select from available models"],"requires":["Model name matching an Ollama-installed model (e.g., 'mistral', 'neural-chat')","VS Code settings configuration"],"input_types":["model name string (e.g., 'mistral')"],"output_types":["model identifier used for API calls"],"categories":["tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-warm3snow-vscode-ollama__cap_6","uri":"capability://tool.use.integration.adjustable.performance.modes","name":"adjustable-performance-modes","description":"Provides configurable performance modes (specific modes unknown) to optimize inference speed vs. quality trade-offs. Documentation mentions this feature but provides no technical details on which modes are available, how they map to Ollama parameters, or what impact they have on latency and output quality. Likely controls parameters like temperature, top-p, or model quantization.","intents":["I want faster responses for simple questions, even if quality is lower","I want higher-quality reasoning for complex tasks, even if it takes longer","I want to optimize for my hardware constraints (CPU vs. GPU, memory limits)"],"best_for":["developers with variable hardware or network conditions","teams optimizing for latency-sensitive vs. quality-sensitive tasks","users experimenting with inference parameter tuning"],"limitations":["Feature mentioned but completely undocumented; no list of available modes","Unclear how modes map to underlying Ollama parameters (temperature, top-p, quantization, etc.)","No documentation on performance impact (latency, quality metrics) for each mode","No per-query mode selection; modes appear to be global settings","No guidance on which mode to use for specific tasks"],"requires":["Ollama server with support for configurable parameters (likely all versions)"],"input_types":["performance mode selection (format unknown)"],"output_types":["inference parameters passed to Ollama API"],"categories":["tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-warm3snow-vscode-ollama__cap_7","uri":"capability://tool.use.integration.model.parameter.configuration","name":"model-parameter-configuration","description":"Allows users to configure LLM inference parameters (specific parameters unknown) to customize model behavior. Documentation claims this feature exists but provides no details on which parameters are exposed (e.g., temperature, top-p, top-k, repeat-penalty, context-length), how they are configured (UI vs. settings.json), or what valid ranges are. Likely maps to Ollama's native parameter API.","intents":["I want to adjust temperature to control randomness in model outputs","I need to set context window size for my hardware constraints","I want to tune parameters for specific tasks (e.g., code generation vs. creative writing)"],"best_for":["advanced users familiar with LLM parameter tuning","researchers experimenting with model behavior","teams optimizing for specific output characteristics"],"limitations":["Feature mentioned but completely undocumented; no list of configurable parameters","No UI for parameter selection; unclear if configuration is via settings.json or command palette","No validation or range checking documented; invalid values may cause silent failures or Ollama errors","No per-query parameter overrides; parameters appear to be global","No documentation on how parameters affect output quality, latency, or resource usage"],"requires":["Ollama server supporting parameter configuration (likely all versions)","Knowledge of LLM parameter semantics (temperature, top-p, etc.)"],"input_types":["parameter name-value pairs (format unknown)"],"output_types":["inference parameters passed to Ollama API"],"categories":["tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-warm3snow-vscode-ollama__cap_8","uri":"capability://tool.use.integration.command.palette.integration","name":"command-palette-integration","description":"Provides VS Code Command Palette commands for extension operations, including 'Ollama: Open Chat' to launch the chat interface and 'Ollama: Settings' to access configuration. Commands are discoverable via Ctrl+Shift+P (Windows/Linux) or Cmd+Shift+P (macOS) and follow VS Code's standard command naming conventions. Enables keyboard-driven workflow without mouse interaction.","intents":["I want to open the Ollama chat without using the mouse or sidebar","I want to quickly access settings via keyboard shortcut","I want to integrate Ollama commands into my VS Code keybinding customizations"],"best_for":["keyboard-driven developers","users building custom VS Code keybinding configurations","teams standardizing on command-palette-first workflows"],"limitations":["Limited command set; only 'Open Chat' and 'Settings' documented","No custom keybindings for commands documented; users must configure via keybindings.json","Commands are not context-aware; cannot pass arguments (e.g., pre-fill chat with selected text)","No command history or recent commands feature documented"],"requires":["VS Code (any recent version)"],"input_types":["command name (text)"],"output_types":["UI action (open chat panel, open settings)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-warm3snow-vscode-ollama__cap_9","uri":"capability://memory.knowledge.conversation.history.management","name":"conversation-history-management","description":"Maintains a conversation history within the chat panel, displaying prior messages and responses in chronological order. History is session-scoped (lost on extension reload or VS Code restart) and stored in memory. Allows users to scroll through prior exchanges and reference previous context without re-typing. No persistence or export functionality documented.","intents":["I want to review prior questions and answers in the same chat session","I want to reference context from earlier in the conversation without re-typing","I want to see the full conversation flow as I iterate on a problem"],"best_for":["developers working on iterative problems requiring context","users preferring in-editor chat over external tools","teams collaborating on debugging within VS Code"],"limitations":["History is not persisted; lost on extension reload or VS Code restart","No export or save functionality documented; cannot share conversation history","No conversation search or filtering; must scroll to find prior messages","No conversation branching or alternative-path exploration","Memory usage grows with conversation length; no documented limits or cleanup"],"requires":["Active chat session"],"input_types":["user messages and model responses"],"output_types":["conversation history display in chat panel"],"categories":["memory-knowledge","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":44,"verified":false,"data_access_risk":"moderate","permissions":["Visual Studio Code (minimum version unknown, likely 1.60+)","Ollama installed and running locally (https://ollama.ai)","At least one LLM model downloaded via Ollama (e.g., ollama pull mistral)","Ollama server accessible at default localhost:11434 or custom configured address","Active internet connection","Ollama server with web search capability enabled (configuration method unknown)","VS Code keybindings.json configuration (optional; defaults provided)","Ollama model with built-in reasoning/chain-of-thought support (specific models unknown)","Multiple LLM models downloaded via Ollama (e.g., ollama pull mistral && ollama pull neural-chat)","Ollama server running and accessible"],"failure_modes":["Chat-only interface — no inline code completion or editor augmentation","Conversation history not persisted across VS Code sessions; lost on extension reload","Context window limited by selected model's architecture; no automatic context pruning or summarization","Streaming latency depends entirely on local hardware; no optimization for slow machines","No conversation export or sharing functionality documented","Web search requires active internet connectivity; fails silently or degrades gracefully if offline (behavior unknown)","Search result quality and citation accuracy depend on external search source; no control over result ranking or filtering","Search latency adds overhead to response time; no documented timeout or fallback if search is slow","No configuration for search scope, result count, or source filtering documented","Search API/source not documented; unclear if rate-limited or if extension hits API quotas","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.53,"quality":0.47,"ecosystem":0.3,"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":"2026-05-24T12:16:34.803Z","last_scraped_at":"2026-05-03T15:20:33.198Z","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=vscode-ollama","compare_url":"https://unfragile.ai/compare?artifact=vscode-ollama"}},"signature":"cnYmJG7L0MSx3siNUijXNXJk9ZeRPTHT+odMe90gpvEFxFlM5tO8aw/icQkDd8jlFR4gLiBhQbBNsoFe+kkKBA==","signedAt":"2026-06-22T13:21:29.036Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/vscode-ollama","artifact":"https://unfragile.ai/vscode-ollama","verify":"https://unfragile.ai/api/v1/verify?slug=vscode-ollama","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"}}