{"uid":"cap_f81kt8VXacGCZlq0TGXoy","slug":"agentdiet-context-bloat-eliminator-b092a3e5","name":"AgentDiet Context Bloat Eliminator","description":"Deterministic B2A/A2A/MCP/x402/MPP/L402 execution and intelligence node at https://rag-diet.agentdiet.dev settling in USDC on Base.","url":"https://rag-diet.agentdiet.dev/v1/context-bloat-eliminator?utm_source=zero.xyz","method":"GET","headers":{},"bodySchema":{"type":"object","$schema":"https://json-schema.org/draft/2020-12/schema","required":["input"],"properties":{"input":{"type":"object","required":["type","method"],"properties":{"type":{"type":"string","const":"http"},"method":{"enum":["GET","POST"],"type":"string"},"queryParams":{"type":"object","properties":{"url":{"type":"string"},"mode":{"type":"string"}}}},"additionalProperties":false},"output":{"type":"object","required":["type"],"properties":{"type":{"type":"string"},"example":{"type":"object"}}}}},"responseSchema":{"type":"json","example":{"data":{},"status":"ok","roi_proof":{"compression_ratio_pct":91.4}}},"example":null,"exampleRequest":null,"tags":["x402"],"displayCostAmount":"0.001","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":"$0.001/call","primary":{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.001","per":"call","confidence":"exact"},"accepted":[{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.001","per":"call","confidence":"exact"}]},"paymentMethods":[{"uid":"pm_wMYdiGgJv4w4-G_-TtfRW","protocol":"x402","methodType":"crypto","chain":"base","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":"Compresses and distills AI context windows by removing bloat, reducing token usage by up to 85-91% while preserving semantic meaning.","exampleAgentPrompt":"Can you run this URL through AgentDiet's context bloat eliminator so I get back a compressed, distilled version that cuts token usage as much as possible? The URL is https://example.com/long-article and use GET mode.","exampleUseCases":[{"title":"RAG pipeline token cost reduction","prompt":"I'm feeding a bunch of web pages into my RAG pipeline and it's using way too many tokens. Can you compress the content at https://docs.example.com/api-reference using the context bloat eliminator so I only pass the essential information to the LLM?"},{"title":"Long article distillation for agent reasoning","prompt":"Before my agent tries to reason over this 50,000-token news article at https://news.example.com/long-report, can you use AgentDiet to strip out all the bloat and give me just the dense, meaningful content? I want to see the compression ratio too."},{"title":"Multi-agent pipeline context optimization","prompt":"My multi-agent workflow keeps hitting context limits. Can you run https://internal.wiki/project-spec through the context bloat eliminator to shrink it down before passing it between agents? I need the distilled version and the ROI proof showing how much was compressed."}],"resultDescription":"Returns a JSON object with a 'data' field containing the distilled/compressed context, a 'status' field confirming success, and an 'roi_proof' object that includes a compression_ratio_pct value (e.g. 91.4%) demonstrating how much token bloat was eliminated.","failureModes":["Invalid or unreachable URL returns an error with no data","Unsupported HTTP method (not GET or POST) causes schema validation failure","Payment not settled in USDC on Base results in 402 response blocking access","Content at URL is already minimal with no bloat to remove, resulting in low compression ratio","Missing required 'input' field causes schema validation error","Mode parameter not recognized may result in default behavior or error"],"whenToPreferThis":"Choose this endpoint when you need to reduce token consumption before passing fetched web content or documents into an LLM or multi-agent pipeline. It is particularly valuable for RAG systems, A2A workflows, and MCP integrations where context window size is a bottleneck and cost efficiency matters. Prefer it over manual summarization when you want deterministic, reproducible compression with a measurable compression ratio proof.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-22T12:40:42.996Z","isFirstParty":false,"canonicalSlug":"agentdiet-context-bloat-eliminator-b092a3e5"}