{"uid":"cap_yb6cC01Qk-vO8HxVCZ3o_","slug":"scrapfly-content-group-api-59e86c6c","name":"ScrapFly Content Group API","description":"69 endpoints for data, content generation, and web scraping. Pay per request via x402 on Base chain.","url":"https://scravfly.vercel.app/api/content/group","method":"POST","headers":{},"bodySchema":{"type":"object","required":["text"],"properties":{"task":{"type":"string","description":"task"},"text":{"type":"string","description":"text"}}},"responseSchema":{"type":"object","required":["success"],"properties":{"data":{"type":"object"},"success":{"type":"boolean"}}},"example":null,"exampleRequest":null,"tags":["x402"],"displayCostAmount":"0.15","displayCostAsset":"USDC","priceDynamic":false,"priceHint":null,"priceStatus":"priced","priceSource":"registry","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":"$0.15/call","primary":{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.15","per":"call","confidence":"exact"},"accepted":[{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.15","per":"call","confidence":"exact"}]},"paymentMethods":[{"uid":"pm_UzMnLUrm6quUoBoA5_vQd","protocol":"x402","methodType":"crypto","chain":"base","mode":"charge","costAmount":"0.15","costPer":"request","priority":0,"asset":null,"unit":"request","depositMicros":null,"planRef":null}],"brandName":null,"brandSlug":null,"brandBaseUrl":null,"brandDocsUrl":null,"whatItDoes":"Groups and organizes text content into structured categories or clusters using AI-powered content analysis","exampleAgentPrompt":"Can you group the following article text into thematic sections — I need the paragraphs clustered by topic: 'Climate change affects agriculture in many ways. Solar panels are becoming cheaper. Droughts reduce crop yields. Renewable energy adoption is accelerating. Farmers are adapting to new weather patterns.'","exampleUseCases":[{"title":"News article topic clustering","prompt":"I have a long scraped news article with mixed topics — can you group the text into distinct thematic sections so I can display each topic separately? Here's the full article text: 'Electric vehicles are gaining market share globally. Scientists warn of rising sea levels. Tesla reports record quarterly sales. Coastal cities are investing in flood barriers. EV charging infrastructure is expanding rapidly.'"},{"title":"Product review organization","prompt":"I have a batch of customer reviews all mixed together and I need them grouped by sentiment theme — like product quality, shipping, customer service. Can you group this text for me: 'The item broke after a week. Shipping was super fast. The color was exactly as described. Customer support was unhelpful. Packaging was damaged on arrival. Delivery came a day early.'"},{"title":"Research notes categorization","prompt":"I've got a wall of raw research notes that I need organized into logical groups by subject matter — can you group this text into coherent categories: 'Python uses indentation for blocks. SQL joins combine tables. Machine learning requires training data. Python lists are mutable. Neural networks have hidden layers. SQL indexes improve query speed.'"}],"resultDescription":"Returns a JSON object with a success boolean and a data object containing the grouped/clustered version of the input text, organized into structured categories or sections based on the provided task instruction and content analysis.","failureModes":["Missing required 'text' field returns validation error","Empty or very short text may produce trivial single-group output","Ambiguous or missing 'task' instruction may produce unexpected groupings","Very large text inputs may hit payload size limits","Service unavailability returns non-200 status with success: false"],"whenToPreferThis":"Use this endpoint when you need to automatically organize or cluster unstructured text into logical categories or groups, especially after scraping raw web content that needs structure imposed on it. Prefer this over generic LLM calls when you want a pay-per-request, no-subscription approach to content structuring on the Base chain via x402.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-09-14T18:53:18.944Z","isFirstParty":false}