{"uid":"cap_VFwgZ04w1aQSnewXDQOWp","slug":"datahealth-observer-mcp-813c3b19","name":"DataHealth Observer MCP","description":"The premier global index of 1,069 monetized MCP nodes across 205 specialized subdomains. Gasless USDC runtime settlements via x402 V2 Spec on Base L2. Save 95% token context.","url":"https://api.m2mcent.com/datahealth-observer-mcp/api/process","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"payload":{"type":"string"}}},"responseSchema":{"type":"json","example":{"success":true}},"example":null,"exampleRequest":null,"tags":["x402"],"displayCostAmount":"0.05","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":"$0.05/call","primary":{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.05","per":"call","confidence":"exact"},"accepted":[{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.05","per":"call","confidence":"exact"}]},"paymentMethods":[{"uid":"pm_lNUcFAd96XyZVejGB9_Es","protocol":"x402","methodType":"crypto","chain":"base","mode":"charge","costAmount":"0.05","costPer":"request","priority":0,"asset":"0x833589fCD6eDb6E08f4c7C32D4f71b54bdA02913","unit":"request","depositMicros":null,"planRef":null}],"brandName":null,"brandSlug":null,"brandBaseUrl":null,"brandDocsUrl":null,"whatItDoes":"Monitors and analyzes the health of data pipelines, datasets, or data assets to detect anomalies, quality issues, and integrity problems.","exampleAgentPrompt":"Can you run a data health check on this dataset and tell me if there are any anomalies, missing values, or integrity issues: '{\"records\": [{\"id\": 1, \"value\": null}, {\"id\": 2, \"value\": 42}]}'?","exampleUseCases":[{"title":"Pipeline data quality gate","prompt":"Before I push this batch of records to my warehouse, run a health check on them and flag any nulls, duplicates, or format violations so I can fix them first."},{"title":"ML training data validation","prompt":"I'm about to train a model on this dataset — can you observe its data health and tell me if there are any outliers, schema mismatches, or corrupt entries I should be aware of?"},{"title":"Real-time ingestion monitoring","prompt":"I'm ingesting streaming sensor data and I need you to monitor each batch for health issues like missing timestamps or out-of-range values and report what you find."}],"resultDescription":"Returns a structured health assessment of the submitted data, including detected anomalies, quality scores, integrity flags, descriptions of any issues found, and an overall health status indicating whether the data is fit for use.","failureModes":["Empty or null data field returns a validation error","Malformed JSON in the data string causes a parse failure","Extremely large payloads may timeout or be rejected","Ambiguous data formats may produce low-confidence health scores","Service unavailability returns a 402 or 5xx error"],"whenToPreferThis":"Choose this endpoint when you need automated, programmatic data quality and health observation for arbitrary data payloads — especially in agentic pipelines where data must be validated before downstream processing, model training, or storage. Prefer it over manual inspection or generic validation libraries when you need a single API call that returns a comprehensive health report with anomaly detection.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-09-14T07:13:23.730Z","isFirstParty":false}