{"uid":"cap_W5w2jH0wtg0QzXlJ6FDBG","slug":"tokensqueezer-text-compressor-4cd3f16c","name":"TokenSqueezer Text Compressor","description":"High-throughput text optimizer for agent data streams. Strips filler, collapses JSON, and densifies repetitive prose to cut upstream LLM token spend.","url":"https://tokensqueezer-production.up.railway.app/compress?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"text":{"type":"string","description":"Raw text block to compress"},"format":{"enum":["auto","plain","markdown","json"],"type":"string"},"aggressive":{"type":"boolean"}}},"responseSchema":null,"example":null,"exampleRequest":null,"tags":["x402"],"displayCostAmount":"0.002","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":"$0.002/call","primary":{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.002","per":"call","confidence":"exact"},"accepted":[{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.002","per":"call","confidence":"exact"}]},"paymentMethods":[{"uid":"pm__cU-Ps68TvuXvHFeFVvgV","protocol":"x402","methodType":"crypto","chain":"base","mode":"charge","costAmount":"0.002","costPer":"request","priority":0,"asset":"0x833589fcd6edb6e08f4c7c32d4f71b54bda02913","unit":"request","depositMicros":null,"planRef":null}],"brandName":null,"brandSlug":null,"brandBaseUrl":null,"brandDocsUrl":null,"whatItDoes":"Compresses and densifies text by stripping filler, collapsing JSON, and removing redundancy to reduce LLM token consumption","exampleAgentPrompt":"Can you squeeze this blog post down to use fewer tokens before I send it to GPT-4? Use aggressive mode and treat it as plain text.","exampleUseCases":[{"title":"LLM pipeline cost reduction","prompt":"I'm piping a huge JSON response from an API into Claude and it's burning through tokens. Can you compress it first using auto format detection so I can cut my costs?"},{"title":"Agent memory compaction","prompt":"My agent's context window is getting bloated with repetitive conversation history. Compress this text block aggressively in plain format to trim it down before I stuff it back into the prompt."},{"title":"Markdown document densification","prompt":"Before I feed this markdown README into my RAG pipeline, strip out all the filler and redundant prose — use markdown format and aggressive mode to get it as tight as possible."}],"resultDescription":"Returns a compressed version of the input text with filler removed, repetition collapsed, and overall token footprint reduced. The output preserves the essential meaning while minimizing character and token count, suitable for direct use as input to a downstream LLM.","failureModes":["Empty or missing 'text' field returns a validation error","Unsupported format value outside enum returns a 400-style error","Extremely large text payloads may hit size limits or timeout","Aggressive mode on highly structured data may distort meaning","Network errors from Railway.app hosting causing intermittent 502/503 responses"],"whenToPreferThis":"Choose this endpoint when you need to reduce token spend before sending text to an expensive LLM API, especially in high-throughput agentic pipelines where every token counts. Ideal when the input is verbose, repetitive, or filler-heavy prose or JSON. Not a substitute for semantic summarization — use this when you want to preserve all key information but in a denser form, rather than losing content through summarization.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-11T06:39:33.163Z","isFirstParty":false,"canonicalSlug":"tokensqueezer-text-compressor-4cd3f16c"}