{"uid":"cap_HGbiNfpi37rd-60a47--i","slug":"agenttoll-llm-pack-8d1dd821","name":"AgentToll LLM-Pack","description":"x402 probe surface for llm-pack. Real usage: POST JSON {url, maxTokens?}. Unpaid GET/POST → 402 with PAYMENT-REQUIRED. Supports optional X-Latch-Bid-Usd.","url":"https://agent-toll.agentfetcher.workers.dev/v1/llm-pack","method":"GET","headers":{},"bodySchema":{"type":"object","properties":{"url":{"type":"string","description":"Public URL to convert into token-budgeted LLM-ready markdown for model context / RAG"},"noCache":{"type":"boolean","description":"Skip cache and re-fetch the page"},"maxTokens":{"type":"number","description":"Token budget for packed markdown (500–32000, default 4000)"}}},"responseSchema":null,"example":null,"exampleRequest":null,"tags":["x402"],"displayCostAmount":"0.00375","displayCostAsset":"USDC","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":"$0.00375/call","primary":{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.00375","per":"call","confidence":"exact"},"accepted":[{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.00375","per":"call","confidence":"exact"}]},"paymentMethods":[{"uid":"pm_Ch965MuXlEtVHDdJ5ud90","protocol":"x402","methodType":"crypto","chain":"base","mode":"charge","costAmount":"0.00375","costPer":"request","priority":0,"asset":"0x833589fCD6eDb6E08f4c7C32D4f71b54bdA02913","unit":"request","depositMicros":null,"planRef":null}],"brandName":null,"brandSlug":null,"brandBaseUrl":null,"brandDocsUrl":null,"whatItDoes":"Fetches a public URL and converts its content into token-budgeted, LLM-ready markdown suitable for model context or RAG pipelines","exampleAgentPrompt":"Can you fetch https://docs.example.com/api-reference and convert it to clean LLM-ready markdown with a token budget of 8000 tokens so I can paste it into my RAG pipeline?","exampleUseCases":[{"title":"Research paper summarization for knowledge base","prompt":"Please fetch https://arxiv.org/pdf/2024-example and convert it to markdown with a 6000 token budget so I can add it to our company's research RAG system without exploding context sizes."},{"title":"Product documentation parsing for support agents","prompt":"Can you grab the content from https://help.shopify.com/en/manual/orders and turn it into clean markdown trimmed to 4000 tokens? I need it to feed into my support chatbot's knowledge base."},{"title":"Real-time news ingestion for market analysis","prompt":"Fetch https://news.example.com/tech-sector-update and give me the markdown version under 3000 tokens so our trading agents can quickly ingest the latest market info without token overhead."}],"resultDescription":"Returns token-budgeted markdown derived from the fetched public URL, trimmed to fit within the specified maxTokens limit (500–32000, default 4000). The output is clean, model-ready text suitable for direct injection into LLM context windows or RAG systems.","failureModes":["URL is not publicly accessible or returns non-200 HTTP status","URL points to non-text content (PDF, binary, etc.) that cannot be converted","maxTokens value is outside the allowed range of 500–32000","Payment not completed (x402 flow) after the first free request per IP","Rate limiting or IP-based quota exhaustion","Page content is too large or complex to process within timeout"],"whenToPreferThis":"Prefer this endpoint over raw HTML fetching or generic scrapers when you need clean, token-budgeted markdown ready for direct injection into an LLM prompt or RAG system. Ideal when you want to avoid processing raw HTML yourself, need to control token consumption precisely, or are building an agentic pipeline where web content must be reliably compressible to a known size.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-09-16T00:38:43.930Z","isFirstParty":false}