{"uid":"cap_kJecBy61P-nn851cRD-Nk","slug":"json-to-pydantic-model-converter-532b62f3","name":"JSON to Pydantic Model Converter","description":"The trust layer for AI agents — 250+ independently tested data capabilities across 27 countries. Execute capabilities via REST, MCP, A2A, or x402 micropayments. Every call returns an audit record with cryptographic chain hashing.","url":"https://api.strale.io/x402/v2/json-to-pydantic","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","HEAD","DELETE"],"type":"string"},"queryParams":{"type":"object","required":["json"],"properties":{"json":{"description":"JSON string or object"},"root_name":{"type":"string"}}}},"additionalProperties":false}}},"responseSchema":null,"example":null,"exampleRequest":null,"tags":["x402"],"displayCostAmount":"0.054","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":"$0.054/call","primary":{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.054","per":"call","confidence":"exact"},"accepted":[{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.054","per":"call","confidence":"exact"}]},"paymentMethods":[{"uid":"pm_6qDurXhYe02-QaOlsRYh8","protocol":"x402","methodType":"crypto","chain":"base","mode":"charge","costAmount":"0.054","costPer":"request","priority":0,"asset":"0x833589fCD6eDb6E08f4c7C32D4f71b54bdA02913","unit":"request","depositMicros":null,"planRef":null}],"brandName":null,"brandSlug":null,"brandBaseUrl":null,"brandDocsUrl":null,"whatItDoes":"Converts a JSON string or object into Python Pydantic model class definitions, handling nested objects, arrays, and optional fields.","exampleAgentPrompt":"Can you convert this JSON into Pydantic model classes for me? Use 'UserProfile' as the root model name: {\"id\": 123, \"name\": \"Alice\", \"address\": {\"street\": \"123 Main St\", \"city\": \"Springfield\"}, \"tags\": [\"admin\", \"user\"]}","exampleUseCases":[{"title":"Scaffold models from API response","prompt":"I got this JSON back from a third-party API and need Pydantic models to work with it in my Python service — can you generate the classes? Here's the JSON: {\"orderId\": \"abc-123\", \"customer\": {\"id\": 9, \"email\": \"joe@example.com\"}, \"items\": [{\"sku\": \"X1\", \"qty\": 2}]}, use 'Order' as the root name."},{"title":"Type-safe config model generation","prompt":"Turn this config JSON into Pydantic classes so I can validate it at runtime in my Python app — root name should be 'AppConfig': {\"database\": {\"host\": \"localhost\", \"port\": 5432}, \"debug\": true, \"allowed_hosts\": [\"example.com\"]}"},{"title":"Data pipeline schema modeling","prompt":"I have this sample JSON record from our data pipeline and I need Pydantic models for it — call the root class 'EventRecord': {\"eventId\": \"e-001\", \"timestamp\": \"2024-01-01T00:00:00Z\", \"payload\": {\"userId\": 42, \"action\": \"click\", \"meta\": {\"page\": \"/home\"}}}"}],"resultDescription":"Returns Python source code containing Pydantic model class definitions that reflect the structure of the provided JSON input, including nested models for sub-objects, typed list fields for arrays, and optional field annotations where applicable.","failureModes":["Invalid or malformed JSON input causes a parse error response","Missing required 'json' query parameter returns a validation error","Extremely deeply nested JSON may hit recursion or size limits","Non-standard JSON types (e.g. dates as strings) may not receive specialized Pydantic validators"],"whenToPreferThis":"Choose this endpoint when you need to quickly scaffold Pydantic model classes from existing JSON data or API responses without manually writing Python type definitions. Ideal for developers integrating third-party APIs, building data pipelines, or enforcing type safety in Python projects. Prefer this over manual authoring when the JSON structure is complex or deeply nested.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-09-13T18:51:57.229Z","isFirstParty":false}