{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"claude-opus-4","slug":"claude-opus-4","name":"Claude Opus 4","type":"model","url":"https://www.anthropic.com/claude/opus","page_url":"https://unfragile.ai/claude-opus-4","categories":["llm-apis"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"claude-opus-4__cap_0","uri":"capability://planning.reasoning.extended.thinking.transparent.reasoning","name":"extended-thinking-transparent-reasoning","description":"Enables Claude to expose its internal chain-of-thought process by allocating compute budget to explicit reasoning steps before generating responses. The model spends configurable thinking tokens on problem decomposition, hypothesis testing, and self-correction before committing to output, making reasoning transparent and auditable. This is distinct from standard token generation as thinking tokens are processed separately and can be streamed or hidden from end users.","intents":["I need to see how the model arrived at its answer for debugging or verification","I want the model to spend more time reasoning on complex problems before responding","I need transparent decision-making for high-stakes applications like code review or financial analysis","I want to understand where the model made mistakes in its reasoning chain"],"best_for":["teams building AI systems requiring explainability and auditability","developers debugging model reasoning on complex multi-step problems","enterprises in regulated industries needing transparent AI decision trails"],"limitations":["Extended thinking increases latency significantly — reasoning tokens must be processed before output generation begins","Thinking tokens consume from the same token budget as output, increasing overall API costs","Thinking output is opaque to end users by default — requires explicit API parameter to expose reasoning","No control over thinking depth or strategy — model autonomously allocates reasoning budget"],"requires":["Claude API access (Anthropic platform, Bedrock, Vertex AI, or Microsoft Foundry)","API parameter budget_tokens set to enable thinking (exact threshold unknown)","Support for streaming or non-streaming responses depending on use case"],"input_types":["text prompts","code snippets","complex multi-step problem descriptions","ambiguous or contradictory instructions"],"output_types":["text response with optional thinking tokens exposed","structured reasoning trace (if parsed from API response)","final answer with confidence indicators"],"categories":["planning-reasoning","transparency"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"claude-opus-4__cap_1","uri":"capability://planning.reasoning.adaptive.thinking.complexity.aware.reasoning","name":"adaptive-thinking-complexity-aware-reasoning","description":"Automatically adjusts reasoning effort based on detected task complexity without explicit user configuration. The model analyzes incoming requests and allocates thinking tokens proportionally — spending minimal compute on straightforward queries (e.g., factual lookups) and deep reasoning on complex problems (e.g., multi-step code debugging). This is implemented as a learned routing mechanism that estimates problem difficulty before committing reasoning budget.","intents":["I want fast responses for simple queries without wasting compute on unnecessary reasoning","I need the model to automatically spend more time on hard problems without me tuning parameters","I want to optimize cost by avoiding reasoning overhead on trivial tasks","I need consistent quality across variable-difficulty workloads without manual configuration"],"best_for":["teams running mixed-difficulty workloads (support tickets, code review, analysis) without manual routing","cost-conscious builders wanting automatic reasoning optimization without prompt engineering","applications requiring variable latency tolerance based on query complexity"],"limitations":["Complexity detection is heuristic-based — may misclassify edge cases and over-allocate reasoning to simple queries or under-allocate to deceptively complex ones","No visibility into complexity scoring or reasoning budget allocation — black-box behavior makes debugging difficult","Adaptive thinking is Opus 4.7+ only — not available in older model versions","Cannot override or fine-tune the complexity detection algorithm"],"requires":["Claude Opus 4.7 or later (released April 2026)","API access via claude-opus-4-7 model identifier","No special configuration required — enabled by default"],"input_types":["text prompts of variable complexity","code snippets with varying difficulty","multi-step problem descriptions","questions ranging from factual to analytical"],"output_types":["text response with automatically-allocated reasoning","variable latency based on detected complexity","no explicit complexity score exposed to user"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"claude-opus-4__cap_10","uri":"capability://data.processing.analysis.prompt.caching.cost.reduction.with.reusable.context","name":"prompt-caching-cost-reduction-with-reusable-context","description":"Caches frequently-accessed context (e.g., large documents, code repositories, system prompts) to reduce token costs by up to 90% on subsequent requests. When the same context is reused, cached tokens are charged at 10% of the normal rate. This is implemented via a token-level caching mechanism that identifies repeated token sequences and stores them server-side, avoiding re-processing on subsequent requests.","intents":["I need to reduce costs when repeatedly analyzing the same large document or codebase","I want to use a large system prompt or context without paying full token cost on every request","I need to process multiple queries against the same knowledge base efficiently","I want to optimize costs for applications with stable, reusable context"],"best_for":["applications with stable, reusable context (e.g., analyzing the same codebase repeatedly, customer support with shared knowledge base)","teams processing multiple queries against the same large document","cost-sensitive applications where context reuse is common"],"limitations":["Caching requires context to be identical across requests — even minor changes invalidate the cache","Cache warm-up cost is high — the first request with new context pays full token cost, and subsequent requests save only 90%","Cache TTL is unknown — cached context may expire if not accessed frequently, requiring re-processing","Caching is transparent — users cannot control what gets cached or inspect cache state","Minimum cache size is unknown — very small context may not be cached (overhead not worth it)"],"requires":["Claude API access (prompt caching available on Anthropic platform and some cloud providers)","Reusable context that is identical across multiple requests","API parameter to enable caching (exact parameter varies by SDK)"],"input_types":["large documents or code files (context to be cached)","queries or prompts that reuse the cached context"],"output_types":["responses with reduced token cost (cached tokens charged at 10% rate)","no visible difference in output quality or latency"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"claude-opus-4__cap_11","uri":"capability://automation.workflow.batch.processing.with.cost.savings","name":"batch-processing-with-cost-savings","description":"Processes multiple requests in batch mode with 50% cost savings compared to real-time API calls. Batch requests are queued and processed during off-peak hours, trading latency for cost reduction. This is useful for non-time-sensitive workloads like data analysis, content generation, or code review where responses can be delayed by hours or days.","intents":["I need to process 1000s of requests but don't need immediate responses","I want to reduce costs on non-urgent workloads like data analysis or content generation","I need to process large datasets with the model without paying full API rates","I want to schedule model processing for off-peak hours to optimize costs"],"best_for":["batch data processing and analysis (non-urgent)","content generation at scale (articles, summaries, translations)","code review and analysis of large codebases","cost-sensitive applications where latency is not critical"],"limitations":["Batch processing introduces latency — requests may take hours or days to complete (exact SLA unknown)","No real-time feedback — users cannot see results until the entire batch completes","Batch requests are not suitable for interactive workflows — no streaming or progressive results","Minimum batch size is unknown — very small batches may not qualify for batch pricing","No priority or expedited processing — all batch requests are treated equally"],"requires":["Claude API with batch processing support (available on Anthropic platform)","Batch request format (JSONL file with multiple requests)","Acceptance of latency (hours to days for processing)"],"input_types":["batch requests in JSONL format (multiple API calls)","large datasets or lists of items to process"],"output_types":["batch results file with responses for all requests","results available after batch processing completes (latency varies)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"claude-opus-4__cap_12","uri":"capability://data.processing.analysis.200k.context.window.large.document.processing","name":"200k-context-window-large-document-processing","description":"Processes documents and codebases up to 200,000 tokens (approximately 150,000 words or 50,000 lines of code) in a single request. This enables the model to analyze entire repositories, long documents, or multiple files without truncation. The large context window is implemented via efficient attention mechanisms and is available across all deployment options (API, web, mobile).","intents":["I need to analyze an entire codebase (50,000+ lines) in a single request","I want to process a long document (100+ pages) without splitting it","I need to maintain context across multiple files or chapters","I want the model to understand relationships between distant parts of a document"],"best_for":["software engineering tasks requiring full codebase context (SWE-bench, refactoring)","document analysis and summarization of long texts","research and literature review requiring multiple sources","code review and architecture analysis of large systems"],"limitations":["Larger context increases latency — processing 200K tokens takes longer than processing 10K tokens","Larger context increases cost — all tokens (input and output) are charged, so large context = higher cost per request","The model may lose focus in very large contexts — relevance of distant context decreases with distance","Token counting is approximate — exact token count depends on tokenization and may vary","Not all deployment options support full 200K context — some cloud providers may have lower limits"],"requires":["Claude Opus 4.7 API access (200K context available on all platforms)","Documents or code files to analyze","Sufficient API quota to handle large requests"],"input_types":["large code files or repositories","long documents (text, PDF, markdown)","multiple files concatenated into a single request"],"output_types":["analysis or summaries of large documents","code review or refactoring suggestions","cross-file or cross-document insights"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"claude-opus-4__cap_13","uri":"capability://image.visual.multimodal.document.processing.with.pdf.support","name":"multimodal-document-processing-with-pdf-support","description":"Processes PDF documents, extracting text and analyzing visual layouts, charts, and images within PDFs. The model can read multi-page PDFs, understand document structure, and extract information from both text and visual elements. PDFs are converted to a format compatible with the vision and text processing capabilities, enabling unified multimodal analysis.","intents":["I need to extract text and data from a multi-page PDF document","I want to analyze charts, tables, or diagrams in a PDF","I need to understand the structure and layout of a document","I want to convert a PDF to structured data or markdown"],"best_for":["document processing and data extraction (invoices, contracts, reports)","research paper analysis (extracting figures, tables, citations)","form processing and data entry automation","document classification and routing"],"limitations":["PDF processing quality depends on PDF structure — scanned PDFs (images) may have lower OCR accuracy than text-based PDFs","Complex layouts with multiple columns or unusual formatting may be misinterpreted","Large PDFs (100+ pages) consume significant tokens and may hit context limits","Encrypted or password-protected PDFs cannot be processed","PDF processing is not faster than text processing — no special optimization for PDFs"],"requires":["Claude Opus 4.7 API access with vision support","PDF files in standard formats","Sufficient context window to hold the entire PDF (large PDFs may exceed 200K tokens)"],"input_types":["PDF files (any size, any structure)","text prompts describing what to extract or analyze"],"output_types":["extracted text (markdown or plain text)","structured data (JSON, CSV)","analysis of document content and structure","descriptions of charts, tables, or images"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"claude-opus-4__cap_14","uri":"capability://data.processing.analysis.structured.output.generation.with.json.schema","name":"structured-output-generation-with-json-schema","description":"Generates structured outputs (JSON, XML, etc.) that conform to a provided schema, ensuring outputs are valid and parseable. The model is constrained to generate only outputs that match the schema, preventing malformed or invalid responses. This is implemented via output token constraints that restrict generation to valid schema tokens.","intents":["I need the model to generate JSON that conforms to a specific schema","I want to ensure the model's output is always valid and parseable","I need to extract structured data from text (e.g., entities, relationships)","I want to use the model's output directly in code without parsing or validation"],"best_for":["data extraction and entity recognition","API response generation","structured data generation for downstream processing","applications requiring guaranteed valid output format"],"limitations":["Schema constraints may force the model to generate invalid or nonsensical data if the schema doesn't match the task — e.g., forcing a required field when the model has no valid value","Complex schemas may reduce model quality — the model must balance following the schema with generating accurate content","Schema validation is strict — even minor deviations from the schema cause generation to fail","No explanation or reasoning in structured output — the model cannot add context or caveats outside the schema"],"requires":["Claude API with structured output support","JSON schema defining the required output format","API parameter to enable structured output mode"],"input_types":["text prompts","data to extract or transform","JSON schema defining output format"],"output_types":["JSON or XML output conforming to the schema","guaranteed valid and parseable output"],"categories":["data-processing-analysis","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"claude-opus-4__cap_15","uri":"capability://tool.use.integration.computer.use.tool.for.ui.automation","name":"computer-use-tool-for-ui-automation","description":"Enables the model to interact with computer interfaces (screenshots, mouse clicks, keyboard input) to automate UI-based tasks. The model can see the current screen state, click buttons, type text, and navigate applications. This is implemented as a tool that provides screen capture and input simulation capabilities, allowing the model to autonomously operate applications.","intents":["I need the model to automate repetitive UI tasks (form filling, data entry, navigation)","I want the model to interact with web applications or desktop software autonomously","I need to test a UI by having the model interact with it and report issues","I want the model to perform tasks that don't have API access (legacy systems, web-only tools)"],"best_for":["UI automation and testing","web scraping and data extraction from interactive sites","legacy system automation (no API access)","accessibility testing and user experience analysis"],"limitations":["Computer use is slower than API-based automation — each action (click, type) requires a round-trip to the model","The model cannot understand complex UI patterns without explicit instruction — it may click wrong buttons or enter data in wrong fields","Computer use is fragile — UI changes break automation, and the model cannot adapt to unexpected layouts","Security risk — the model has access to the entire screen, including sensitive information (passwords, tokens, personal data)","No built-in error recovery — if the model clicks the wrong button, it must be manually corrected"],"requires":["Claude API with computer use tool enabled","Screen capture capability (provided by the client application)","Input simulation capability (mouse, keyboard)","Explicit permission to access the screen and input devices"],"input_types":["text instructions describing the task","screenshots of the current UI state"],"output_types":["sequence of UI actions (clicks, typing, navigation)","final result after completing the task","error messages if the task fails"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"claude-opus-4__cap_16","uri":"capability://memory.knowledge.memory.tool.for.persistent.context.across.sessions","name":"memory-tool-for-persistent-context-across-sessions","description":"Provides a memory tool that allows the model to store and retrieve information across multiple conversations or sessions. The model can save facts, preferences, or context to memory and retrieve them in future interactions, enabling persistent personalization and context accumulation. Memory is implemented as a key-value store that the model can read and write to via tool calls.","intents":["I need the model to remember user preferences or context across multiple conversations","I want the model to accumulate knowledge about a user or project over time","I need to provide the model with persistent context without including it in every prompt","I want the model to learn from previous interactions and apply that learning to new tasks"],"best_for":["personalized assistants that interact with users over time","long-running projects where context accumulates across sessions","applications requiring user-specific customization","agents that learn and improve from experience"],"limitations":["Memory is not automatically managed — the model must explicitly decide what to save and retrieve, which adds complexity","Memory size is limited — exact limits unknown, but very large memory stores may cause latency or cost issues","Memory is not versioned or timestamped — the model cannot easily track when information was stored or how it has changed","Memory is not shared across users — each user has separate memory, limiting collaborative learning","Memory retrieval is not semantic — the model must know the exact key to retrieve information, not search by meaning"],"requires":["Claude API with memory tool support","Persistent storage backend for memory (provided by the application)","User or session identifier to scope memory"],"input_types":["text to save to memory (facts, preferences, context)","memory keys to retrieve"],"output_types":["retrieved memory values","confirmation of saved memory"],"categories":["memory-knowledge","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"claude-opus-4__cap_2","uri":"capability://tool.use.integration.agentic.multi.step.tool.orchestration","name":"agentic-multi-step-tool-orchestration","description":"Orchestrates complex multi-step workflows by chaining tool calls across extended interactions, maintaining coherence and state across dozens of steps. The model can invoke tools in parallel, handle tool failures with retry logic, and maintain context about previous tool results to inform subsequent decisions. This is implemented via a managed agent infrastructure that persists session state, tracks tool execution history, and enables autonomous operation for hours without human intervention.","intents":["I need an AI to autonomously execute a multi-day project (e.g., data analysis, code refactoring, research) with minimal supervision","I want the model to recover from tool failures and retry with different strategies","I need to track what tools the model called and in what order for audit trails","I want the model to maintain context across 50+ tool invocations without losing coherence"],"best_for":["teams building autonomous agents for software engineering (SWE-bench tasks, codebase refactoring)","enterprises automating multi-day workflows (data pipelines, research, content generation)","developers needing long-running task execution without session management overhead"],"limitations":["Long-running agents consume tokens continuously — a 10-hour autonomous task may consume millions of tokens, making cost unpredictable","No built-in persistence across API restarts — if the client connection drops, session state is lost unless explicitly saved","Tool failures are not automatically recovered — model must be prompted to implement retry logic, adding complexity","Latency compounds across steps — each tool call adds network round-trip time, making 100-step workflows potentially slow","No guaranteed determinism — same input may produce different tool call sequences on different runs"],"requires":["Claude API access with tool use support (Anthropic platform, Bedrock, Vertex AI, or Microsoft Foundry)","Tool definitions provided via JSON schema (OpenAI-compatible function calling format)","Client-side orchestration logic to handle tool results and feed them back to the model","Persistent session storage if multi-day execution is required (external database or file system)"],"input_types":["text instructions describing multi-step goals","tool definitions (JSON schemas)","tool execution results (text, structured data, error messages)","file paths or API endpoints for tools to operate on"],"output_types":["sequence of tool calls with arguments","final text response after all tools complete","structured execution trace (tool name, arguments, results, timestamps)","error messages if tools fail"],"categories":["tool-use-integration","planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"claude-opus-4__cap_3","uri":"capability://tool.use.integration.parallel.tool.execution.with.streaming","name":"parallel-tool-execution-with-streaming","description":"Invokes multiple tools concurrently within a single model response, with fine-grained streaming of tool calls and results. The model can batch independent tool invocations (e.g., fetch 5 URLs in parallel) and stream results back to the client as they complete, rather than waiting for all tools to finish. This reduces latency for I/O-bound workflows and enables real-time progress feedback.","intents":["I need to fetch multiple URLs or query multiple APIs in parallel without sequential delays","I want to see tool calls stream in real-time as the model decides what to invoke","I need to cancel or interrupt tool execution if the model goes off-track","I want to reduce latency on workflows with independent tool calls (e.g., batch data fetching)"],"best_for":["applications with I/O-bound tool calls (web scraping, API aggregation, database queries)","real-time interactive agents where users need to see reasoning progress","cost-sensitive workflows where streaming allows early termination if the model diverges"],"limitations":["Parallel tool execution requires client-side concurrency handling — the SDK must manage multiple tool invocations and merge results back into the conversation","Streaming adds complexity to error handling — if one of N parallel tools fails, the model must decide whether to retry, skip, or abort the entire workflow","Tool result ordering is non-deterministic — results arrive in completion order, not invocation order, requiring client-side buffering to maintain consistency","Not all tool types support parallel execution — stateful tools (e.g., file editors) may conflict if run concurrently"],"requires":["Claude API with streaming support (all platforms: Anthropic, Bedrock, Vertex AI, Microsoft Foundry)","Client-side code to handle streaming responses and manage concurrent tool execution","Tool definitions that support parallel invocation (stateless tools preferred)"],"input_types":["text prompts requesting multiple independent actions","tool definitions with parallelization hints (optional metadata)"],"output_types":["streamed tool calls (one per chunk)","streamed tool results as they complete","final text response after all tools finish"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"claude-opus-4__cap_4","uri":"capability://tool.use.integration.strict.tool.use.mode.guaranteed.invocation","name":"strict-tool-use-mode-guaranteed-invocation","description":"Enforces that the model MUST invoke a specified tool before generating free-form text, preventing the model from bypassing tool use or hallucinating tool results. When strict mode is enabled, the model's output is constrained to valid tool invocations only — it cannot refuse to use the tool or generate text that pretends the tool was called. This is implemented via output token constraints that restrict the model's generation vocabulary to valid tool schemas.","intents":["I need to guarantee the model calls a specific tool (e.g., database query) before answering a user question","I want to prevent the model from hallucinating tool results or pretending it executed a tool","I need to enforce a workflow where certain tools are mandatory (e.g., always check the database before responding)","I want to reduce hallucination by forcing the model to ground answers in tool results"],"best_for":["applications requiring mandatory tool use (e.g., always query the database, always check the API)","workflows where hallucinated tool results are unacceptable (financial systems, medical applications)","teams building guardrailed agents where certain tools must be invoked before user-facing responses"],"limitations":["Strict mode prevents the model from declining to use a tool even if it's not applicable — may force unnecessary tool invocations on edge cases","The model cannot explain why it's invoking the tool or provide context before the invocation — output is tool-only","Strict mode is all-or-nothing — cannot enforce multiple tools or conditional tool use within a single response","If the tool schema is overly broad or poorly defined, the model may invoke it with nonsensical arguments just to satisfy the constraint"],"requires":["Claude API with tool use support","Tool definition provided as JSON schema","API parameter to enable strict tool use mode (exact parameter name varies by SDK)"],"input_types":["text prompts","tool definitions (JSON schema)"],"output_types":["single tool invocation (guaranteed)","no free-form text output in strict mode"],"categories":["tool-use-integration","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"claude-opus-4__cap_5","uri":"capability://code.generation.editing.code.generation.with.swe.bench.optimization","name":"code-generation-with-swe-bench-optimization","description":"Generates production-ready code with specialized optimization for software engineering tasks, achieving 72.5% on SWE-bench (solving real GitHub issues in open-source repositories). The model is trained to understand large codebases, identify root causes of bugs, generate minimal diffs, and test changes before committing. This is distinct from generic code generation because it combines extended thinking for problem analysis with tool use for code execution and testing.","intents":["I need to automatically fix bugs in a large codebase by analyzing the issue and generating a minimal diff","I want the model to understand the full context of a repository before suggesting code changes","I need the model to test its changes before returning them (e.g., run unit tests)","I want to automate code review by having the model identify issues and suggest fixes"],"best_for":["teams automating bug fixes in large codebases (SWE-bench-style tasks)","developers using AI for code refactoring and technical debt reduction","open-source maintainers automating issue resolution","enterprises building internal code automation tools"],"limitations":["SWE-bench performance (72.5%) means ~27.5% of real GitHub issues are not solved correctly — complex issues with architectural changes remain challenging","Code generation quality depends heavily on codebase documentation and test coverage — poorly documented repos yield lower-quality fixes","The model cannot understand proprietary or domain-specific code patterns without examples in the context window","Generated code may not follow team conventions or style guides — requires post-processing or linting","Testing is limited to what tools are available — if the codebase has no test suite, the model cannot validate changes"],"requires":["Claude Opus 4.7 API access","Full codebase context (or at least the relevant files) provided to the model","Tool definitions for code execution, testing, and version control (git)","200K+ context window to handle large files and full repository context"],"input_types":["GitHub issue descriptions (text)","code files (any language)","test files and test output","error messages and stack traces","repository structure and documentation"],"output_types":["code diffs (unified format)","generated code files","test results and validation output","explanations of changes"],"categories":["code-generation-editing","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"claude-opus-4__cap_6","uri":"capability://image.visual.vision.analysis.with.image.input","name":"vision-analysis-with-image-input","description":"Analyzes images, diagrams, charts, and screenshots by processing visual input alongside text prompts. The model can extract text from images (OCR), identify objects and relationships, analyze code in screenshots, and reason about visual layouts. Vision is integrated into the same API as text, allowing seamless multimodal workflows where images and text are processed together in a single request.","intents":["I need to extract text from a screenshot or scanned document","I want the model to analyze a diagram or chart and explain what it shows","I need to debug a UI issue by analyzing a screenshot of the application","I want to extract code from an image and convert it to editable text"],"best_for":["developers debugging UI issues using screenshots","teams automating document processing (invoices, receipts, forms)","researchers analyzing charts and diagrams from papers or reports","content creators extracting code or text from images"],"limitations":["Vision performance on complex diagrams or handwritten text is lower than on clean screenshots or printed text","Image resolution affects accuracy — low-resolution images may fail OCR or object detection","The model cannot process video — only static images","Vision input counts toward the token limit — a high-resolution image may consume 1000+ tokens, reducing context available for text","No explicit image understanding metrics provided — vision quality is not benchmarked separately"],"requires":["Claude Opus 4.7 API access (vision is available in all deployment options)","Images in supported formats (JPEG, PNG, GIF, WebP)","Image size limits (exact limits vary by platform but typically 5MB per image)"],"input_types":["images (JPEG, PNG, GIF, WebP)","text prompts describing what to analyze in the image","multiple images in a single request"],"output_types":["text descriptions of image content","extracted text (OCR)","structured data extracted from images (e.g., table data)","code extracted from screenshots"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"claude-opus-4__cap_7","uri":"capability://search.retrieval.web.search.and.fetch.tool.integration","name":"web-search-and-fetch-tool-integration","description":"Provides built-in web search and web fetch tools that the model can invoke to retrieve current information from the internet. The model can search for information, fetch full page content, and synthesize results into responses. These tools are available through the standard tool-use API, allowing the model to autonomously decide when to search the web based on the user's query.","intents":["I need the model to search the web for current information (news, prices, availability)","I want the model to fetch and summarize content from specific URLs","I need to ground the model's responses in real-time data rather than training data","I want the model to verify claims by searching the web before responding"],"best_for":["applications requiring current information (news, stock prices, weather, availability)","research assistants that need to verify claims with web sources","customer support bots that need to look up current policies or product information","content creators needing to cite sources and verify facts"],"limitations":["Web search results are limited to what search engines index — private or paywalled content is not accessible","Web fetch may fail on JavaScript-heavy sites or sites with anti-scraping measures","Search results are not real-time — there is a delay between content publication and search engine indexing","The model must decide autonomously when to search — it cannot be forced to search for every query, and may search unnecessarily for simple questions","Web search adds latency — each search query requires a network request to the search engine"],"requires":["Claude API access (web search/fetch tools are available on Anthropic platform and some cloud providers)","Internet connectivity (the model's infrastructure must be able to reach search engines and web servers)","No explicit API key required — web search is built-in to the model"],"input_types":["text prompts requesting information","URLs to fetch"],"output_types":["search results (snippets and links)","fetched page content (text extracted from HTML)","synthesized response combining web results with model reasoning"],"categories":["search-retrieval","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"claude-opus-4__cap_8","uri":"capability://code.generation.editing.code.execution.tool.with.bash.and.python","name":"code-execution-tool-with-bash-and-python","description":"Executes code (Python, Bash, and other languages) in a sandboxed environment and returns output to the model. The model can write code, execute it, see results, and iterate based on output. This enables the model to test hypotheses, validate changes, and debug code interactively. Code execution is provided as a tool that the model can invoke, not as a native capability.","intents":["I need the model to run code and see the output to validate its logic","I want the model to debug code by executing it and analyzing error messages","I need the model to perform calculations or data transformations by executing code","I want the model to test its generated code before returning it to the user"],"best_for":["data analysis and scientific computing workflows","code debugging and validation","automated testing and validation of generated code","interactive problem-solving where the model needs to iterate on code"],"limitations":["Code execution is sandboxed — the model cannot access the user's file system or external services (unless explicitly allowed)","Execution timeout limits apply — long-running code may be terminated","No persistent state between executions — each code block runs in isolation (though variables persist within a session)","Output is limited to stdout/stderr — graphical output or interactive prompts are not supported","Security: the sandboxed environment prevents malicious code from escaping, but the model could still generate code that consumes resources or attempts exploits"],"requires":["Claude API with code execution tool enabled","Sandboxed execution environment (provided by Anthropic or cloud provider)","Python 3.x and Bash available in the sandbox"],"input_types":["code snippets (Python, Bash, etc.)","data files or input for the code to process"],"output_types":["code execution output (stdout)","error messages (stderr)","return values or results"],"categories":["code-generation-editing","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"claude-opus-4__cap_9","uri":"capability://automation.workflow.managed.agents.stateful.session.persistence","name":"managed-agents-stateful-session-persistence","description":"Provides a managed agent infrastructure that persists session state, maintains event history, and enables autonomous operation across multiple API calls. Sessions store conversation history, tool execution results, and agent state, allowing the agent to resume work without losing context. 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