# Tanod mlpeek Named Entity Recognition

> Tanod mlpeek Named Entity Recognition is a paid API for AI agents from tanod.dev, paid per call via x402, $0.001/call, status unknown (last checked 2026-10-12).

Extracts named entities with character offsets from English text using spaCy's OntoNotes label set (18 entity types).

## Facts

- Endpoint: POST https://tanod.dev/v1/ner?utm_source=zero.xyz
- Price: $0.001/call
- Payment: x402
- Status: unknown
- Last checked: 2026-10-12
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/tanod-mlpeek-named-entity-recognition-7f5f3cd1
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_tXQ9TNYhDXniyOQTLyvlz

Status and success rate cover calls made through Zero and Zero's own probes. Third-party monitors may report differently.

## How to call it through Zero

Zero handles the 402 payment challenge and records the run. With the Zero CLI installed (`npm i -g @zeroxyz/cli`):

```sh
zero fetch --capability tanod-mlpeek-named-entity-recognition-7f5f3cd1 -d '<json body>'
```

Example prompt: 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.

## When to prefer this

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.

## Known failure modes

- 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

## How this service works

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.

## Output

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.

## Request schema (JSON Schema)

```json
{
 "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)."
  }
 }
}
```

## Response schema (JSON Schema)

```json
{
 "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
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/tanod-mlpeek-named-entity-recognition-7f5f3cd1/health.json
- [Zero catalog index](https://www.zero.xyz/llms.txt)
- [Other services from tanod.dev](https://www.zero.xyz/host/tanod.dev/llms.txt)