{"uid":"cap_AH50UUIXA0xUbkLB-DStW","slug":"delx-approx-eq-9a442225","name":"Delx Approx Eq","description":"Approximate equality within tolerance. Call when you compare floats from agent math without exact-equality traps. Returns equal flag, a, b, tolerance for $0.001 USDC via x402 on Base. No network, no keys, no retention; first-party local JSON only. Results are advisory; the caller owns budgets, auth, and production controls.","url":"https://api.delx.ai/api/v1/x402/approx-eq","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"a":{"type":"number","description":"Input field: a."},"b":{"type":"number","description":"Input field: b."},"tolerance":{"type":"number","description":"Input field: tolerance."}}},"responseSchema":{"type":"json","example":{"a":1,"b":1.0004,"equal":true,"schema":"delx/util-approx-eq/v1","tolerance":0.001}},"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_9GfretgMcUjJQlrGjV1B5","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":"Checks whether two floating-point numbers are approximately equal within a specified tolerance, returning a boolean equal flag along with the inputs.","exampleAgentPrompt":"Check if 0.1 plus 0.2 is approximately equal to 0.3 — use a tolerance of 0.0001 so we don't get tripped up by floating-point rounding.","exampleUseCases":[{"title":"Float rounding check in agent pipeline","prompt":"I just computed two separate paths through a pricing formula and got 19.99999998 and 20.0 — can you check if those are approximately equal within a tolerance of 0.00001?"},{"title":"Validate physics simulation output","prompt":"My simulation returned 9.80664 for gravitational acceleration and I expected 9.80665 — are those close enough to be considered equal with a tolerance of 0.0001?"},{"title":"Financial calculation drift detection","prompt":"Two different fee calculation methods gave me 1234.5670001 and 1234.567 — check if they're approximately equal within 0.001 so I can decide if the difference matters."}],"resultDescription":"Returns a JSON object containing a boolean 'equal' flag indicating whether the two numbers fall within the specified tolerance of each other, plus the original input values a, b, and tolerance for reference.","failureModes":["Missing required fields (a, b, or tolerance) result in a validation error","Non-numeric inputs for a, b, or tolerance will be rejected","Negative tolerance values may produce unexpected behavior","Payment failure via x402 will block the request","Extremely large or NaN/Infinity float values may cause edge-case errors"],"whenToPreferThis":"Choose this endpoint when your agent performs floating-point arithmetic and needs to avoid exact-equality pitfalls common with IEEE 754 floats. It is ideal for lightweight, stateless numerical comparisons within an agentic pipeline where you want a definitive advisory signal without implementing epsilon-comparison logic yourself. At $0.001 USDC it is cost-effective for high-frequency checks in automated workflows.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-09-13T07:02:22.264Z","isFirstParty":false}