# scopeapi.dev Text Embeddings

> scopeapi.dev Text Embeddings is a paid API for AI agents from scopeapi.dev, paid per call via x402, $0.003/call, status unknown (last checked 2026-09-14).

Converts one or more text strings into high-dimensional vector embeddings using a 1536-dimension model

## Facts

- Endpoint: POST https://scopeapi.dev/embeddings
- Price: $0.003/call
- Payment: x402
- Status: unknown
- Last checked: 2026-09-14
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/scopeapi-dev-text-embeddings-af3a6d0c
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_JU1zWpwvaW4uWIrSkzJQE

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 scopeapi-dev-text-embeddings-af3a6d0c -d '<json body>'
```

Example prompt: Convert this sentence into a text embedding vector: 'The quick brown fox jumps over the lazy dog' — I need the 1536-dimension float array to store in my vector database.

## When to prefer this

Choose this endpoint when you need fast, affordable text vectorization via a pay-per-call x402 micropayment model without managing API keys. It is ideal for agents that need to embed text on-demand within a pipeline, store vectors in a database, or perform semantic similarity at $0.003 per call. Prefer this over raw OpenAI API access when operating in an agentic marketplace context where x402 payments are already wired in.

## Known failure modes

- Input array exceeds 16 items (maxItems constraint violated)
- Empty input array (minItems:1 not satisfied)
- Malformed JSON body causing parse error
- Token limit exceeded for very long input strings
- Payment failure via x402 protocol returning 402 status
- Network timeout for large batch requests

## How this service works

Text Embeddings

## Output

Returns a JSON object containing the model name (text-embedding-3-small), the number of dimensions (1536), token usage statistics (prompt tokens and total tokens), and an array of embedding objects each with an index and a floating-point vector array representing the semantic content of the input text.

## Request schema (JSON Schema)

```json
{
 "type": "object",
 "$schema": "https://json-schema.org/draft/2020-12/schema",
 "required": [
  "input"
 ],
 "properties": {
  "input": {
   "type": "object",
   "required": [
    "type",
    "method",
    "bodyType",
    "body"
   ],
   "properties": {
    "body": {
     "required": [
      "input"
     ],
     "properties": {
      "input": {
       "oneOf": [
        {
         "type": "string"
        },
        {
         "type": "array",
         "items": {
          "type": "string"
         },
         "maxItems": 16,
         "minItems": 1
        }
       ]
      }
     }
    },
    "type": {
     "type": "string",
     "const": "http"
    },
    "method": {
     "enum": [
      "POST"
     ],
     "type": "string"
    },
    "bodyType": {
     "enum": [
      "json",
      "form-data",
      "text"
     ],
     "type": "string"
    }
   },
   "additionalProperties": false
  },
  "output": {
   "type": "object",
   "required": [
    "type"
   ],
   "properties": {
    "type": {
     "type": "string"
    },
    "example": {
     "type": "object"
    }
   }
  }
 }
}
```

## Response schema (JSON Schema)

```json
{
 "type": "json",
 "example": {
  "model": "text-embedding-3-small",
  "usage": {
   "totalTokens": 6,
   "promptTokens": 6
  },
  "endpoint": "/embeddings",
  "dimensions": 1536,
  "embeddings": [
   {
    "index": 0,
    "embedding": [
     0.0123,
     -0.0456,
     0.0789
    ]
   }
  ]
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/scopeapi-dev-text-embeddings-af3a6d0c/health.json
- [Zero catalog index](https://www.zero.xyz/llms.txt)
- [Other services from scopeapi.dev](https://www.zero.xyz/host/scopeapi.dev/llms.txt)