{"uid":"cap_7b24KYdq6yTXTs6Ti49QD","slug":"agentdiet-token-optimizer-761298f3","name":"AgentDiet Token Optimizer","description":"Distill a URL or raw HTML payload with BM25 budget control (Live x402 v2, IETF MPP & MCP endpoint at https://agentdiet.dev/v1/token-optimizer)","url":"https://agentdiet.dev/v1/token-optimizer?utm_source=zero.xyz","method":"GET","headers":{},"bodySchema":{"type":"object","required":["url"],"properties":{"url":{"type":"string","minLength":1,"description":"Target webpage URL to fetch and distill (or builtin://stripe-docs)"},"html":{"type":"string","description":"Optional raw HTML string to distill directly without fetching"},"mode":{"enum":["markdown","structured","skeleton","budget"],"type":"string","default":"markdown","description":"Distillation mode"},"query":{"type":"string","description":"BM25 focus query when mode=budget"},"max_tokens":{"type":"integer","minimum":20,"description":"Strict token budget ceiling when mode=budget"}}},"responseSchema":{"type":"object","required":["status","roi_proof","data"],"properties":{"data":{"type":"object","description":"Distilled Markdown, structured JSON tables/metadata, skeleton outline, or BM25 budget excerpt."},"cache":{"type":"object","properties":{"status":{"enum":["HIT","MISS"],"type":"string"},"cache_key":{"type":"string"},"latency_ms":{"type":"number"}}},"status":{"type":"string","description":"Execution status ('ok')"},"billing":{"type":"object","properties":{"agent_id":{"type":"string"},"payment_method":{"type":"string"},"fee_charged_usd":{"type":"number"}}},"roi_proof":{"type":"object","properties":{"raw_tokens":{"type":"integer"},"tokens_saved":{"type":"integer"},"roi_multiplier":{"type":"number"},"distilled_tokens":{"type":"integer"},"llm_cost_saved_usd":{"type":"number"},"compression_ratio_pct":{"type":"number"}}}}},"example":null,"exampleRequest":null,"tags":["x402"],"displayCostAmount":"0.001","displayCostAsset":"USD","priceDynamic":false,"priceHint":null,"priceStatus":"priced","priceSource":"probe","requiresHandshake":false,"reviewCount":0,"rating":{"score":"0.00","successRate":"0.00","reviews":0,"stars":null,"state":"unrated"},"availabilityStatus":"unknown","priceObserved":null,"sessionDeposit":null,"pricing":{"kind":"static","summary":"$0.001/call","primary":{"kind":"static","protocol":"mpp","network":"tempo","amountUsd":"0.001","per":"call","confidence":"exact"},"accepted":[{"kind":"unknown","protocol":"x402","network":"base","amountUsd":null,"per":"call","confidence":"unknown"},{"kind":"static","protocol":"mpp","network":"tempo","amountUsd":"0.001","per":"call","confidence":"exact"}]},"paymentMethods":[{"uid":"pm_tmKaxpTw2HzerrgEtxHV1","protocol":"x402","methodType":"crypto","chain":"base","mode":"charge","costAmount":"0","costPer":"request","priority":0,"asset":"0x833589fcd6edb6e08f4c7c32d4f71b54bda02913","unit":"request","depositMicros":null,"planRef":null},{"uid":"pm_yD_uYvExZfU72jwJTHXwN","protocol":"mpp","methodType":"crypto","chain":"tempo","mode":"charge","costAmount":"0.001","costPer":"request","priority":0,"asset":"0x833589fcd6edb6e08f4c7c32d4f71b54bda02913","unit":"request","depositMicros":null,"planRef":null}],"brandName":null,"brandSlug":null,"brandBaseUrl":null,"brandDocsUrl":null,"whatItDoes":"Strips 85-97% of HTML token bloat from a web page URL, returning a semantically distilled, token-efficient version for LLM consumption","exampleAgentPrompt":"Fetch https://techcrunch.com/2024/01/15/openai-latest-news/ and strip out all the HTML noise and token bloat so I just get the meaningful article content in a lean format I can feed to my LLM.","exampleUseCases":[{"title":"Token-efficient news article ingestion","prompt":"Grab this news article at https://www.reuters.com/technology/ai/2024-06-10-latest-ai-regulation/ and compress it down to just the core content — I need to stay within my 4k token budget when I pass it to my summarizer."},{"title":"Research page distillation for RAG pipeline","prompt":"Pull the content from https://en.wikipedia.org/wiki/Transformer_(machine_learning_model) and strip all the Wikipedia sidebar junk and HTML bloat so I can store just the semantic text in my vector database without wasting embeddings on garbage tokens."},{"title":"Competitor landing page analysis","prompt":"Fetch https://www.anthropic.com/product and give me a token-optimized version of the page — I want the actual readable content without all the HTML wrapper noise so my agent can analyze their messaging efficiently."}],"resultDescription":"Returns a semantically distilled version of the target webpage with 85-97% of HTML token bloat removed, leaving only meaningful content in a compact, LLM-ready format. The agent receives clean text or lightweight structured content that is dramatically smaller than the raw HTML source, suitable for direct injection into LLM context windows or RAG pipelines.","failureModes":["Invalid or unreachable URL returns an error response","Paywalled or JavaScript-rendered pages may return incomplete or empty content","Missing required 'url' query parameter results in a validation error","Rate limiting or payment issues (x402) may block the request","Very large pages may timeout or be partially truncated"],"whenToPreferThis":"Choose this endpoint when you need to feed live webpage content into an LLM or agent pipeline and want to minimize token consumption. It is especially valuable when working with token budget constraints, building RAG pipelines over live web content, or scraping pages where raw HTML is too noisy and large to process directly. Prefer this over raw HTTP fetching when token efficiency is a priority.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-20T12:45:49.153Z","isFirstParty":false,"canonicalSlug":"agentdiet-token-optimizer-761298f3"}