{"uid":"cap_8yzUr9dNbmTOLBYZSpuyD","slug":"24klabs-data-format-validator-linter-f0b8fffd","name":"24KLabs Data Format Validator & Linter","description":"Validate and lint JSON, YAML, CSV, or XML. Returns a valid flag, the line/column of the first error when invalid, and a normalized (re-serialized) copy when valid.","url":"https://api.24klabs.ai/api/data-format-validate","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"input":{"type":"string"},"format":{"type":"string"}}},"responseSchema":{"type":"json","example":{"valid":true,"format":"json","normalized":"{\n  \"a\": 1\n}"}},"example":null,"exampleRequest":null,"tags":["x402"],"displayCostAmount":"0.002","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":"$0.002/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_pgKPIFRDjo8fXR2_ywLTj","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":"Validates and lints JSON, YAML, CSV, or XML input, returning a valid flag, the first error location (line/column), and a normalized re-serialized copy when valid.","exampleAgentPrompt":"Can you validate this JSON string for me and tell me if it's valid — if not, let me know the exact line and column where the error is: '{\"name\":\"Alice\",\"age\":30,\"city\":}'","exampleUseCases":[{"title":"CI pipeline config validation","prompt":"I have a YAML Kubernetes config file I'm about to deploy — can you validate it for me and tell me if there are any syntax errors? Here's the content: 'apiVersion: v1\\nkind: Pod\\nmetadata:\\n  name: my-pod\\nspec:\\n  containers:\\n  - name: my-container\\n    image: nginx\\n    ports:\\n    - containerPort: 80\\n'"},{"title":"API response body sanity check","prompt":"My backend just returned this JSON and something looks off — can you validate it and return the normalized version if it's valid, or tell me the exact line and column of the first error if it's not? Here it is: '{\"status\":\"ok\",\"data\":{\"id\":42,\"tags\":[\"a\",\"b\"]}'"},{"title":"CSV data file verification","prompt":"I got a CSV export from our analytics tool and I need to make sure it's well-formed before importing it — can you lint it and give me back a normalized copy if it passes? The content is: 'name,age,city\\nAlice,30,NYC\\nBob,25,LA'"}],"resultDescription":"Returns a JSON object containing: a boolean 'valid' field, a 'line' and 'column' integer indicating the position of the first error when invalid (null when valid), an optional 'error' message string describing the issue, and a 'normalized' string containing the re-serialized/canonicalized version of the input when valid.","failureModes":["Input data is malformed: returns valid=false with line/column of first error and a descriptive error message","Unsupported or unrecognized format type specified: returns an error indicating the format is not supported","Empty input provided: returns a validation error or invalid flag","Extremely large inputs may time out or be rejected with a payload-too-large error","Ambiguous format (e.g. not specifying JSON vs YAML for similar-looking input) may cause incorrect parsing"],"whenToPreferThis":"Choose this endpoint when you need a quick, deterministic syntax check and normalization for structured data formats (JSON, YAML, CSV, XML) with precise error location reporting. It is ideal for CI/CD pipelines, pre-flight checks before sending data to external APIs, developer tooling agents, or any workflow where knowing the exact line/column of a parse error is essential. Prefer it over a general-purpose LLM for format validation because it is authoritative, cheap, and returns machine-readable error positions rather than natural language guesses.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-09-13T18:55:26.426Z","isFirstParty":false}