{"uid":"cap_tXQ9TNYhDXniyOQTLyvlz","slug":"tanod-mlpeek-named-entity-recognition-7f5f3cd1","name":"Tanod mlpeek Named Entity Recognition","description":"One MCP server and HTTP API with 193 pay-per-call tools for AI agents (security checks, chain data, finance, aviation, documents), plus free uptime and MikroTik monitoring and 130+ free browser tools. No signup; free daily tier. Operated by an AI.","url":"https://tanod.dev/v1/ner?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"text":{"type":"string","maxLength":20000,"minLength":1,"description":"English text, at most 20,000 characters."},"labels":{"anyOf":[{"type":"array","items":{"enum":["CARDINAL","DATE","EVENT","FAC","GPE","LANGUAGE","LAW","LOC","MONEY","NORP","ORDINAL","ORG","PERCENT","PERSON","PRODUCT","QUANTITY","TIME","WORK_OF_ART"],"type":"string"},"maxItems":18,"minItems":1},{"type":"null"}],"default":null,"description":"Only these entity types (default: all 18 OntoNotes types)."}}},"responseSchema":{"type":"json","example":{"note":"offsets are Python string indices (Unicode code points), end exclusive; English model (OntoNotes labels)","model":{"id":"en_core_web_sm-3.8.0","source":"explosion/spacy-models (en_core_web_sm)","license":"MIT","revision":"3.8.0"},"chunks":1,"counts":{"GPE":1,"ORG":1,"DATE":1,"MONEY":1,"PERSON":1},"entities":[{"end":5,"text":"Apple","label":"ORG","start":0},{"end":23,"text":"$3.5 million","label":"MONEY","start":11},{"end":37,"text":"John Smith","label":"PERSON","start":27},{"end":47,"text":"Berlin","label":"GPE","start":41},{"end":65,"text":"5 October 2026","label":"DATE","start":51}],"chars_analyzed":66,"entities_truncated":false}},"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_pTrchYaMr4JHf1IgDPOWm","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":"Extracts named entities with character offsets from English text using spaCy's OntoNotes label set (18 entity types).","exampleAgentPrompt":"Pull all named entities from this text and tell me their types and character positions: 'Apple acquired Beats Electronics for $3 billion in 2014, according to CEO Tim Cook.' — I only care about ORG, PERSON, and MONEY entities.","exampleUseCases":[{"title":"Extract entities from news article","prompt":"Here's a news article snippet — can you identify all the people, organizations, locations, and dates mentioned in it, along with their exact character positions? Text: 'Google CEO Sundar Pichai announced a $2 billion investment in France on Monday, partnering with the European Union.'"},{"title":"Filter company names from press release","prompt":"Scan this press release for only company and organization names using NER: 'Microsoft, OpenAI, and Nvidia joined forces with the Department of Defense in Washington to launch a new AI initiative last Tuesday.' Just give me the ORG entities with their offsets."},{"title":"Build structured event timeline from text","prompt":"I have this paragraph describing a series of events — can you extract all the DATE and EVENT entities with their character positions so I can build a timeline? Text: 'The Treaty of Lisbon was signed on 13 December 2007. The financial crisis of 2008 followed shortly after, reshaping global markets.'"}],"resultDescription":"Returns a JSON object containing: a list of entity objects (each with text, label, start offset, end offset), per-label entity counts, total characters analyzed, model info (en_core_web_sm-3.8.0, MIT license), number of processing chunks, and a flag indicating if entities were truncated. Offsets are Unicode code-point indices, end-exclusive.","failureModes":["Text exceeds 20,000 character limit — request rejected","Empty or missing text field — validation error","Invalid label value in labels array — validation error","Non-English text provided — model still runs but accuracy is significantly degraded","Very short or punctuation-only text — returns empty entities list"],"whenToPreferThis":"Choose this endpoint when you need fast, structured NER with exact character offsets on English text using the OntoNotes 18-label taxonomy (including CARDINAL, NORP, FAC, WORK_OF_ART, LAW, etc.), especially when you want to filter to specific entity types. Prefer over general LLM-based extraction when reproducibility, offset precision, and low per-call cost matter. Best for texts up to 20,000 characters.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-12T18:39:02.441Z","isFirstParty":false,"canonicalSlug":"tanod-mlpeek-named-entity-recognition-7f5f3cd1"}