{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"anthropic-api","slug":"anthropic-api","name":"Anthropic API","type":"mcp","url":"https://console.anthropic.com","page_url":"https://unfragile.ai/anthropic-api","categories":["llm-apis"],"tags":[],"pricing":{"model":"usage","free":false,"starting_price":"$0.25/1M tokens"},"status":"active","verified":false},"capabilities":[{"id":"anthropic-api__cap_0","uri":"capability://text.generation.language.turn.by.turn.conversational.messaging.with.200k.token.context","name":"turn-by-turn conversational messaging with 200k token context","description":"Implements a stateless Messages API that accepts JSON-formatted conversation turns with role-based message routing (user/assistant). Maintains conversation history within a single request payload, supporting up to 200,000 tokens of context per request. Returns streamed or buffered text responses with configurable max_tokens output limits. Handles multi-turn dialogue without server-side session state, requiring clients to manage conversation history.","intents":["I need to build a chatbot that remembers the full conversation context without managing sessions on my backend","I want to send a long document plus follow-up questions in a single API call without losing context","I need to implement a conversational interface where the client controls conversation state"],"best_for":["developers building stateless chat applications","teams integrating Claude into existing conversation management systems","builders prototyping multi-turn reasoning workflows"],"limitations":["200K token context window is fixed per request — no persistent memory across API calls unless client manages history","Stateless design requires client to reconstruct full conversation history for each request, increasing payload size and latency for long conversations","No built-in conversation persistence or session management — requires external database for production chat applications"],"requires":["Anthropic API key from console.anthropic.com","HTTP client supporting JSON request/response","One of: Python SDK, TypeScript SDK, Go SDK, Java SDK, Ruby SDK, PHP SDK, or cURL"],"input_types":["text","images (via vision capability)","files (via Files API integration)"],"output_types":["text","structured JSON (via structured outputs capability)"],"categories":["text-generation-language","conversational-ai"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"anthropic-api__cap_1","uri":"capability://tool.use.integration.parallel.and.sequential.tool.calling.with.strict.schema.enforcement","name":"parallel and sequential tool calling with strict schema enforcement","description":"Implements a tool-calling system where Claude receives a JSON schema registry of available functions, generates structured tool_use blocks within responses, and can invoke multiple tools in parallel within a single turn. Supports 'strict' mode that enforces exact schema compliance, preventing hallucinated parameters. Tool results are fed back via user messages with tool_result blocks, creating a request-response loop. Integrates with prompt caching to avoid re-transmitting tool schemas on repeated calls.","intents":["I need Claude to call multiple APIs in parallel (e.g., fetch user data AND check inventory) in a single turn","I want to guarantee Claude only generates valid function calls that match my exact schema, with no parameter hallucination","I need to build an agentic workflow where Claude decides which tools to use and processes results iteratively"],"best_for":["developers building AI agents with deterministic tool integration","teams implementing function-calling workflows that require strict schema validation","builders creating multi-step automation where tool hallucination is costly"],"limitations":["Strict mode enforces schema but adds latency overhead (~50-100ms per call) due to validation","Parallel tool execution is logical (Claude generates multiple tool_use blocks) but requires client to execute them concurrently — no server-side parallelization","Tool schemas must be re-transmitted in every request unless prompt caching is enabled, increasing token usage for large tool registries","No built-in error recovery — failed tool calls require explicit client handling and re-prompting"],"requires":["Anthropic API key","Tool definitions as JSON schemas (OpenAI function-calling format compatible)","Client code to execute tools and feed results back via tool_result messages"],"input_types":["JSON tool schemas","tool_use response blocks from Claude"],"output_types":["tool_use blocks with function name and parameters","text responses after tool execution"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"anthropic-api__cap_10","uri":"capability://code.generation.editing.code.execution.tool.for.runtime.verification.and.testing","name":"code execution tool for runtime verification and testing","description":"Provides a 'code execution' tool that Claude can invoke to run Python code and receive output, enabling runtime verification of code correctness, testing of algorithms, and interactive problem-solving. Claude writes code, executes it, sees results, and iterates. Execution happens in a sandboxed environment with output captured and returned to Claude.","intents":["I need Claude to verify code correctness by running it and checking output","I want Claude to test algorithms or solve math problems by executing code","I need interactive debugging where Claude can run code, see errors, and fix them"],"best_for":["developers building coding tutors or homework helpers","teams automating code verification and testing","builders creating interactive problem-solving systems"],"limitations":["Sandbox environment is restricted — no file system access, network calls, or system commands","Execution timeout unknown — long-running code may be killed","Python-only (no JavaScript, Java, etc.) — limits language support","No package installation — only standard library and pre-installed packages available","Output size limits unknown — large outputs may be truncated","Security: code execution in shared sandbox may have isolation concerns"],"requires":["Anthropic API key","Python code to execute","Acceptance of sandbox limitations"],"input_types":["Python code snippets"],"output_types":["execution output (stdout/stderr)","error messages"],"categories":["code-generation-editing","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"anthropic-api__cap_11","uri":"capability://data.processing.analysis.files.api.for.document.handling.and.multipart.uploads","name":"files api for document handling and multipart uploads","description":"Provides a Files API endpoint for uploading documents (PDFs, text, images) that can be referenced in subsequent API calls. Files are stored server-side and can be used across multiple requests without re-uploading. Supports multipart form uploads and returns file IDs for reference. Integrates with vision and text processing to enable document analysis workflows.","intents":["I need to upload a large PDF once and reference it in multiple API calls without re-uploading","I want to build a document analysis system where users upload files and ask questions about them","I need to process multiple files in a batch without embedding them in each request"],"best_for":["developers building document management systems","teams processing large files that are reused across requests","builders creating document Q&A or analysis applications"],"limitations":["File size limits not specified — large files may be rejected or require chunking","File retention period unknown — files may expire and require re-upload","No file versioning — updated files require new uploads with new IDs","File access control unknown — unclear if files are isolated per user or API key","Supported file formats not fully documented — PDF and images confirmed, others unknown"],"requires":["Anthropic API key","HTTP client supporting multipart form uploads","Files in supported formats (PDF, images, text)"],"input_types":["PDF files","images","text documents"],"output_types":["file IDs for reference in subsequent requests"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"anthropic-api__cap_12","uri":"capability://tool.use.integration.model.context.protocol.mcp.server.integration.for.tool.extensibility","name":"model context protocol (mcp) server integration for tool extensibility","description":"Implements MCP as a standard for connecting external tools and data sources to Claude. MCP servers expose tools, resources, and prompts via a standardized protocol; Claude can invoke them through the tool-calling system. Anthropic provides MCP connectors for common services (databases, APIs, file systems) and supports custom MCP server implementations. Enables modular, reusable tool ecosystems without modifying Claude's core API.","intents":["I need to connect Claude to external tools and data sources (databases, APIs, file systems) without building custom integrations","I want to build a reusable tool ecosystem that works across multiple Claude deployments","I need to extend Claude's capabilities with domain-specific tools (e.g., medical databases, financial APIs)"],"best_for":["teams building enterprise AI systems with complex tool ecosystems","developers creating reusable tool libraries and frameworks","builders integrating Claude into existing tool infrastructure"],"limitations":["MCP is a new standard (Anthropic-originated) — ecosystem is still developing, limited pre-built connectors","Requires running MCP servers separately from Claude API — adds operational complexity","Protocol overhead adds latency for each tool invocation compared to direct API calls","Error handling and retry logic must be implemented in MCP servers — no built-in resilience","Documentation and examples are limited — requires understanding MCP protocol details"],"requires":["Anthropic API key","MCP server implementation (custom or pre-built connector)","Client code to connect MCP servers to Claude API","Understanding of MCP protocol (JSON-RPC over stdio or HTTP)"],"input_types":["MCP server definitions","tool schemas from MCP servers"],"output_types":["tool invocations via MCP protocol","tool results from MCP servers"],"categories":["tool-use-integration","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"anthropic-api__cap_13","uri":"capability://planning.reasoning.managed.agents.api.for.stateful.multi.turn.agent.workflows","name":"managed agents api for stateful, multi-turn agent workflows","description":"Provides a stateful agent infrastructure where Claude maintains conversation state, event history, and tool execution context across multiple turns without client-side session management. Agents can be configured with system prompts, tools, and resource limits. Clients send messages and receive responses; the API handles state persistence, tool invocation, and event logging. Enables building complex, long-running agents without managing conversation history.","intents":["I need to build a stateful agent that remembers context across many turns without managing conversation history","I want to create a customer support agent that maintains state and handles complex multi-step workflows","I need an agent infrastructure with built-in event logging and audit trails"],"best_for":["teams building production AI agents with complex state management","developers creating customer-facing agent systems","builders requiring audit trails and event logging for compliance"],"limitations":["Managed Agents API is separate from Messages API — requires different client code and mental model","State management is opaque — limited visibility into internal agent state and decision-making","Configuration options unknown — unclear how much control clients have over agent behavior","Pricing for Managed Agents likely differs from Messages API — cost model unknown","Documentation is limited — fewer examples and best practices compared to Messages API"],"requires":["Anthropic API key","Managed Agents API access (may require separate signup or tier)","Agent configuration (system prompt, tools, resource limits)"],"input_types":["user messages","agent configuration"],"output_types":["agent responses","event history","execution logs"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"anthropic-api__cap_14","uri":"capability://data.processing.analysis.embeddings.generation.for.semantic.search.and.similarity","name":"embeddings generation for semantic search and similarity","description":"Provides an embeddings endpoint that converts text into fixed-size vector representations (embeddings) suitable for semantic search, clustering, and similarity comparison. Embeddings capture semantic meaning, enabling finding similar documents or concepts without keyword matching. Integrates with external vector databases (Pinecone, Weaviate, etc.) for storage and retrieval.","intents":["I need to build a semantic search system that finds similar documents based on meaning, not keywords","I want to cluster documents or concepts based on semantic similarity","I need to implement a recommendation system based on semantic similarity"],"best_for":["developers building semantic search and RAG systems","teams implementing recommendation engines","builders creating document clustering or similarity analysis"],"limitations":["Embedding model details unknown — unclear what model is used or how to optimize for specific domains","Vector dimension unknown — affects storage and retrieval performance in vector databases","Batch embedding limits unknown — maximum number of texts per request unclear","No fine-tuning support — embeddings are fixed and cannot be customized for specific domains","Requires external vector database for storage and retrieval — adds operational complexity"],"requires":["Anthropic API key","Text to embed","External vector database (Pinecone, Weaviate, Milvus, etc.) for storage and retrieval"],"input_types":["text strings"],"output_types":["vector embeddings (fixed-size arrays)"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"anthropic-api__cap_15","uri":"capability://text.generation.language.streaming.responses.for.real.time.output.and.reduced.latency","name":"streaming responses for real-time output and reduced latency","description":"Supports streaming responses where Claude's output is returned incrementally as it's generated, rather than waiting for the complete response. Client receives chunks of text (or tool_use blocks) in real-time, enabling progressive display and reduced perceived latency. Streaming works with all API features (tool-calling, vision, structured outputs). Reduces time-to-first-token and enables cancellation of long-running requests.","intents":["I need to display Claude's response in real-time as it's being generated (like ChatGPT)","I want to reduce perceived latency by showing partial results immediately","I need to cancel long-running requests if the user stops waiting"],"best_for":["developers building interactive chat interfaces","teams creating real-time AI applications","builders optimizing for user experience and perceived latency"],"limitations":["Streaming adds complexity to client code — requires handling partial responses and buffering","Tool-calling with streaming is more complex — tool_use blocks must be accumulated before execution","Structured outputs with streaming may be incomplete — JSON may be partial until stream ends","Error handling is more complex — errors may occur mid-stream, requiring recovery logic","Streaming prevents certain optimizations (e.g., response caching) that require complete responses"],"requires":["Anthropic API key","HTTP client supporting streaming responses (Server-Sent Events or chunked transfer encoding)","Client code to handle partial responses and accumulate chunks"],"input_types":["text prompts","streaming request flag"],"output_types":["streamed text chunks","streamed tool_use blocks"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"anthropic-api__cap_16","uri":"capability://safety.moderation.streaming.refusals.for.transparent.content.policy.enforcement","name":"streaming refusals for transparent content policy enforcement","description":"Enables Claude to refuse requests (e.g., harmful content, policy violations) while streaming, returning refusal messages in real-time rather than after processing. Refusals are streamed like normal responses, providing transparency about why a request was declined. Integrates with streaming responses for consistent behavior.","intents":["I need to show users why their request was declined in real-time","I want transparent content policy enforcement without hidden filtering","I need to log refusals for compliance and monitoring"],"best_for":["developers building transparent AI systems","teams requiring compliance and audit trails","builders creating user-facing applications with clear policy enforcement"],"limitations":["Refusal criteria are opaque — unclear what triggers refusals or how to avoid them","Refusals are not customizable — cannot override or adjust policy enforcement","Streaming refusals add latency compared to buffered refusals","No refusal analytics — limited visibility into refusal patterns or false positives"],"requires":["Anthropic API key","Streaming response handling","Client code to detect and handle refusal messages"],"input_types":["text prompts that may trigger refusals"],"output_types":["refusal messages (streamed)"],"categories":["safety-moderation","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"anthropic-api__cap_17","uri":"capability://data.processing.analysis.token.counting.api.for.cost.estimation.and.optimization","name":"token counting api for cost estimation and optimization","description":"Provides a token counting endpoint that calculates the number of tokens in a message without making an API call, enabling cost estimation before sending requests. Supports counting tokens for messages, tool schemas, and cached content. Enables clients to optimize prompts, estimate costs, and make decisions about request batching or caching.","intents":["I need to estimate API costs before sending a request","I want to optimize prompts to reduce token usage and costs","I need to decide whether to use prompt caching based on token savings"],"best_for":["developers building cost-conscious AI applications","teams optimizing API spending","builders implementing dynamic prompt optimization"],"limitations":["Token counting is approximate — actual tokens may differ slightly due to tokenization edge cases","Requires separate API call — adds latency to cost estimation workflow","No batch token counting — must count tokens for each message separately","Caching token calculations are complex — unclear how to accurately estimate cached token savings"],"requires":["Anthropic API key","Message content to count tokens for"],"input_types":["text messages","tool schemas","images"],"output_types":["token count (integer)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"anthropic-api__cap_2","uri":"capability://image.visual.computer.use.automation.via.vision.based.tool","name":"computer use automation via vision-based tool","description":"Provides a 'computer use' tool that enables Claude to interact with desktop/web interfaces by receiving screenshots, analyzing them with vision capabilities, and generating mouse/keyboard actions (click, type, scroll). Claude sees the screen state, reasons about UI elements, and issues action commands that are executed by client code, creating a feedback loop. Integrates with vision model to understand complex UI layouts and extract information from visual elements.","intents":["I need Claude to automate repetitive UI tasks like filling forms, navigating websites, or interacting with desktop applications","I want to build a bot that can understand and interact with any web interface without API access","I need to test web applications by having Claude interact with them visually and report issues"],"best_for":["teams automating legacy system interactions without API access","developers building RPA (Robotic Process Automation) solutions","QA engineers implementing visual regression and interaction testing"],"limitations":["Requires screenshot capture and transmission for each action, creating high latency (500ms-2s per interaction) compared to API-based automation","Vision-based understanding can fail on complex or novel UI patterns, requiring fallback manual intervention","No built-in OCR optimization — may struggle with small text or low-contrast UI elements","Requires client-side execution environment with screen capture and input control capabilities (not available in browser-only contexts)","Rate-limited by vision processing speed and screenshot transmission bandwidth"],"requires":["Anthropic API key","Client environment with screen capture capability (desktop/server, not browser)","Input control library (e.g., pyautogui for Python, xdotool for Linux)","Vision-capable Claude model (Opus or Sonnet)"],"input_types":["screenshots (PNG/JPEG)","action history from previous steps"],"output_types":["action commands (click coordinates, text input, scroll direction)","reasoning about UI state"],"categories":["image-visual","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"anthropic-api__cap_3","uri":"capability://image.visual.vision.and.image.analysis.with.multi.format.support","name":"vision and image analysis with multi-format support","description":"Accepts images (JPEG, PNG, GIF, WebP) as base64-encoded content blocks within messages, enabling Claude to analyze visual content, extract text (OCR), identify objects, read charts/diagrams, and answer questions about images. Supports multiple images per message and mixed text-image conversations. Vision processing is integrated into the same Messages API, requiring no separate endpoint.","intents":["I need Claude to read text from screenshots, PDFs, or photos (OCR capability)","I want to analyze charts, diagrams, or infographics and extract structured data","I need to build a visual Q&A system where users upload images and ask questions about them"],"best_for":["developers building document processing pipelines","teams automating visual data extraction (invoices, receipts, charts)","builders creating image-based search or analysis applications"],"limitations":["Image size limits not specified in documentation — large images may be downsampled or rejected","OCR quality depends on image resolution and contrast; small text (<8pt) may be unreliable","No built-in image preprocessing — requires client to handle rotation, compression, and format conversion","Vision processing adds latency (~500-1000ms) compared to text-only requests","No batch image processing — each image requires a separate API call unless bundled in a single message"],"requires":["Anthropic API key","Images in supported formats: JPEG, PNG, GIF, WebP","Base64 encoding of image data for transmission"],"input_types":["images (JPEG, PNG, GIF, WebP)","text queries about images"],"output_types":["text descriptions and analysis","extracted text (OCR)","structured data (JSON via structured outputs)"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"anthropic-api__cap_4","uri":"capability://data.processing.analysis.structured.output.generation.with.json.schema.validation","name":"structured output generation with json schema validation","description":"Enables Claude to generate responses constrained to a specified JSON schema, ensuring output is always valid, parseable JSON matching the provided structure. Client defines schema (e.g., object with specific fields and types), and Claude generates responses that conform exactly to that schema. Validation happens at generation time, preventing invalid outputs. Integrates with tool-calling for deterministic function parameter generation.","intents":["I need Claude to extract structured data (entities, relationships) from unstructured text and return it as valid JSON","I want to guarantee API responses are always parseable without try-catch error handling","I need to generate function parameters or API payloads with strict type validation"],"best_for":["developers building data extraction pipelines","teams integrating Claude into systems requiring deterministic output formats","builders creating structured data generation workflows"],"limitations":["Schema complexity is limited — deeply nested or recursive schemas may cause generation failures","Schema validation adds ~50-100ms latency due to constraint enforcement during generation","No schema versioning or migration support — schema changes require client-side handling","Enum validation is strict — Claude cannot generate values outside specified enum lists, limiting flexibility"],"requires":["Anthropic API key","JSON schema definition (JSON Schema format)","Client code to parse and validate returned JSON"],"input_types":["text prompts","JSON schema definitions"],"output_types":["JSON objects matching specified schema"],"categories":["data-processing-analysis","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"anthropic-api__cap_5","uri":"capability://memory.knowledge.prompt.caching.for.repeated.context.reuse","name":"prompt caching for repeated context reuse","description":"Caches large, frequently-repeated context blocks (documents, system prompts, tool schemas) at the API level, reducing token consumption and latency for subsequent requests using the same context. Uses content hashing to identify cacheable blocks, storing them server-side for 5 minutes. Subsequent requests with the same cached content pay only 10% of the token cost for cached blocks, plus a small cache-write cost on first use. Works transparently with all API features (tool-calling, vision, structured outputs).","intents":["I need to reduce costs when repeatedly querying the same large document or knowledge base","I want to speed up API responses by caching system prompts and tool schemas across multiple requests","I need to optimize token usage for agentic workflows that reuse the same context"],"best_for":["teams processing high-volume requests with repeated context (e.g., customer support bots with shared knowledge base)","developers building agents that reuse large tool registries or system prompts","builders optimizing costs for document-heavy workflows"],"limitations":["Cache TTL is 5 minutes — cached content expires and must be re-transmitted, limiting long-running session benefits","Cache key is content-based (hash) — minor changes to context invalidate the cache, requiring re-transmission","Minimum cache size threshold not specified — small context blocks may not be cached","Cache write cost (~25% of normal token cost) applies on first use, reducing savings for one-off requests","No cache invalidation API — cannot manually clear cache, must wait for 5-minute expiration"],"requires":["Anthropic API key","Context blocks larger than minimum threshold (exact size unknown)","Repeated requests within 5-minute window using identical context"],"input_types":["text context blocks","tool schemas","system prompts"],"output_types":["reduced token consumption (90% savings on cached blocks)","faster response times"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"anthropic-api__cap_6","uri":"capability://automation.workflow.batch.processing.api.for.asynchronous.high.volume.requests","name":"batch processing api for asynchronous high-volume requests","description":"Accepts batches of up to 10,000 API requests in JSONL format, processes them asynchronously with lower per-token costs (50% discount), and returns results in JSONL format. Requests are queued and processed during off-peak hours, with results available via polling or webhook. Enables cost-effective processing of non-time-sensitive workloads like data extraction, summarization, or content generation at scale.","intents":["I need to process thousands of documents for extraction or summarization at lower cost","I want to run large-scale content generation jobs (e.g., product descriptions) without paying real-time API rates","I need to batch process user requests overnight and deliver results the next morning"],"best_for":["teams running large-scale data processing pipelines","developers building batch content generation systems","builders optimizing costs for non-real-time workloads"],"limitations":["Asynchronous processing introduces unpredictable latency (hours to days) — unsuitable for real-time applications","Batch size limited to 10,000 requests — larger workloads require multiple batch submissions","No built-in retry logic — failed requests must be manually resubmitted","Results are returned in JSONL format — requires client-side parsing and correlation with original requests","Webhook support status unknown — may require polling for result retrieval"],"requires":["Anthropic API key","Batch requests formatted as JSONL (one JSON object per line)","Each request must be a valid Messages API call","Polling mechanism or webhook handler for result retrieval"],"input_types":["JSONL file with Messages API requests"],"output_types":["JSONL file with Messages API responses"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"anthropic-api__cap_7","uri":"capability://planning.reasoning.extended.thinking.for.complex.reasoning.and.problem.solving","name":"extended thinking for complex reasoning and problem-solving","description":"Enables Claude to perform multi-step reasoning before generating responses, showing internal thought process in a separate 'thinking' block. Claude allocates computational budget to reasoning, working through problems step-by-step before answering. Useful for math, logic, code debugging, and complex analysis. Thinking blocks are visible to users, providing transparency into reasoning. Integrates with all other API features.","intents":["I need Claude to solve complex math or logic problems with visible reasoning steps","I want to debug code by having Claude think through the logic before suggesting fixes","I need to verify Claude's reasoning for high-stakes decisions (medical, legal, financial)"],"best_for":["developers building educational or tutoring systems","teams requiring explainable AI for compliance or verification","builders solving complex technical problems (math, algorithms, debugging)"],"limitations":["Extended thinking adds significant latency (2-5x slower than normal requests) due to reasoning overhead","Thinking blocks consume tokens and increase API costs — exact overhead unknown","Reasoning quality depends on problem complexity — may not improve performance on simple tasks","Thinking process is not editable or controllable — Claude determines reasoning depth automatically","No way to limit thinking budget — all requests with extended thinking enabled use full reasoning"],"requires":["Anthropic API key","Claude Opus or Sonnet model (Haiku may not support extended thinking)","Client code to parse and display thinking blocks"],"input_types":["text prompts","code snippets","math problems"],"output_types":["thinking blocks (internal reasoning)","text responses (final answer)"],"categories":["planning-reasoning","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"anthropic-api__cap_8","uri":"capability://planning.reasoning.adaptive.thinking.for.dynamic.computational.effort.allocation","name":"adaptive thinking for dynamic computational effort allocation","description":"Beta feature that enables Claude to dynamically allocate computational effort based on problem complexity, spending more reasoning cycles on hard problems and less on easy ones. Unlike extended thinking (which uses fixed reasoning budget), adaptive thinking adjusts effort automatically. Useful for mixed-difficulty workloads where some requests need deep reasoning and others don't.","intents":["I need Claude to spend more time on hard problems and less on easy ones automatically","I want to optimize latency and cost by having Claude adjust reasoning depth per request","I need a system that handles variable-difficulty tasks without manual tuning"],"best_for":["teams processing mixed-difficulty workloads","developers optimizing for variable latency tolerance","builders seeking automatic effort allocation without manual configuration"],"limitations":["Beta/experimental status — not recommended for production use","Effort allocation algorithm is opaque — no control over reasoning depth","Latency and cost are unpredictable — requests may take 1-10x longer depending on perceived difficulty","No metrics for effort allocation — cannot measure or audit computational spending","Interaction with other features (caching, batching) unknown"],"requires":["Anthropic API key","Claude Opus or Sonnet model","Acceptance of experimental/beta status and potential breaking changes"],"input_types":["text prompts of variable difficulty"],"output_types":["text responses with variable latency"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"anthropic-api__cap_9","uri":"capability://search.retrieval.web.search.and.fetch.tools.for.real.time.information.retrieval","name":"web search and fetch tools for real-time information retrieval","description":"Provides 'web search' and 'web fetch' tools that Claude can invoke to search the internet and retrieve current information. Web search returns ranked results with snippets; web fetch retrieves full page content. Tools are invoked via the tool-calling system, with results fed back to Claude for synthesis. Enables Claude to answer questions about current events, recent data, or information not in training data.","intents":["I need Claude to answer questions about current events or recent news","I want to build a system that retrieves and synthesizes information from multiple websites","I need Claude to verify claims against current data or check real-time prices/availability"],"best_for":["developers building research or fact-checking systems","teams creating news aggregation or monitoring applications","builders needing real-time data integration without custom API wrappers"],"limitations":["Web search results are ranked by relevance but may include outdated or unreliable sources","Web fetch may fail on JavaScript-heavy sites or paywalled content","Search results are limited to snippets — full content requires separate fetch call, adding latency","No built-in source verification or fact-checking — Claude must evaluate source credibility","Rate limits on web requests unknown — high-volume searches may be throttled","Privacy concerns — search queries and fetched content are transmitted to Anthropic servers"],"requires":["Anthropic API key","Internet connectivity on Anthropic servers","Client code to handle tool invocation and result processing"],"input_types":["search queries","URLs to fetch"],"output_types":["search results with snippets","full page content","synthesized answers"],"categories":["search-retrieval","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"anthropic-api__headline","uri":"capability://llm.apis.mcp.api.for.claude.models.with.long.context.and.strong.coding.capabilities","name":"mcp api for claude models with long context and strong coding capabilities","description":"The Anthropic API provides access to Claude models, known for their ability to handle long contexts, strong coding capabilities, and advanced safety features, making it ideal for developers seeking robust AI solutions.","intents":["best LLM API","LLM API for coding tasks","MCP server for AI models","Claude API for long context processing","best API for safe AI interactions"],"best_for":["developers needing long context processing","projects requiring safety features"],"limitations":[],"requires":["API key"],"input_types":["text"],"output_types":["text","structured outputs"],"categories":["llm-apis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":78,"verified":false,"data_access_risk":"high","permissions":["Anthropic API key from console.anthropic.com","HTTP client supporting JSON request/response","One of: Python SDK, TypeScript SDK, Go SDK, Java SDK, Ruby SDK, PHP SDK, or cURL","Anthropic API key","Tool definitions as JSON schemas (OpenAI function-calling format compatible)","Client code to execute tools and feed results back via tool_result messages","Python code to execute","Acceptance of sandbox limitations","HTTP client supporting multipart form uploads","Files in supported formats (PDF, images, text)"],"failure_modes":["200K token context window is fixed per request — no persistent memory across API calls unless client manages history","Stateless design requires client to reconstruct full conversation history for each request, increasing payload size and latency for long conversations","No built-in conversation persistence or session management — requires external database for production chat applications","Strict mode enforces schema but adds latency overhead (~50-100ms per call) due to validation","Parallel tool execution is logical (Claude generates multiple tool_use blocks) but requires client to execute them concurrently — no server-side parallelization","Tool schemas must be re-transmitted in every request unless prompt caching is enabled, increasing token usage for large tool registries","No built-in error recovery — failed tool calls require explicit client handling and re-prompting","Sandbox environment is restricted — no file system access, network calls, or system commands","Execution timeout unknown — long-running code may be killed","Python-only (no JavaScript, Java, etc.) — limits language support","builder identity is not verified yet","no observed match outcomes 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