# MolCloud Text Embedding API

> MolCloud Text Embedding API is a paid API for AI agents from api.molcloud.net, paid per call via x402, $0.003/call, status unknown (last checked 2026-10-17).

Converts input text into a 768-dimensional vector embedding for semantic similarity, search, and retrieval tasks

## Facts

- Endpoint: POST https://api.molcloud.net/api/ai/embed?utm_source=zero.xyz
- Price: $0.003/call
- Payment: x402
- Status: unknown
- Last checked: 2026-10-17
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/molcloud-text-embedding-api-e83ceaac
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_HD0bNDWDCnuVTnoOqKlVw

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 molcloud-text-embedding-api-e83ceaac -d '<json body>'
```

Example prompt: Turn this sentence into a vector embedding so I can store it in my vector database: 'The quick brown fox jumps over the lazy dog.'

## When to prefer this

Choose this endpoint when you need pay-per-use text embeddings without a subscription to OpenAI, Cohere, or similar providers, especially in autonomous agent pipelines that settle micropayments in USDC on Base via x402. Ideal for RAG pipelines, semantic search indexing, and similarity scoring where you need 768-dimensional vectors and want crypto-native billing with no API key management.

## Known failure modes

- Empty or missing 'text' field returns an error response
- Extremely long text may exceed token limits and return a truncation or error
- Payment failure via x402 (insufficient USDC balance on Base) results in 402 Payment Required before processing
- Network timeout on edge gateway returns 503 or similar
- Malformed JSON body returns 400 Bad Request

## How this service works

41 pay-per-request AI, search, headless browser, crypto alpha & memory APIs for autonomous agents settling in USDC on Base via x402 v2.

## Output

Returns a JSON object containing a 'data' field with the 768-dimensional float vector array and the dimension count, plus a 'status' field confirming success and the endpoint path. The vector can be directly inserted into any vector store or used for cosine similarity calculations.

## Request schema (JSON Schema)

```json
{
 "type": "object",
 "properties": {
  "text": {
   "type": "string",
   "description": "Input text to convert into a vector embedding"
  }
 }
}
```

## Response schema (JSON Schema)

```json
{
 "type": "json",
 "example": {
  "data": {
   "vector": [
    0.012,
    -0.034
   ],
   "dimensions": 768
  },
  "status": "success",
  "endpoint": "/api/ai/embed"
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/molcloud-text-embedding-api-e83ceaac/health.json
- [Zero catalog index](https://www.zero.xyz/llms.txt)
- [Other services from api.molcloud.net](https://www.zero.xyz/host/api.molcloud.net/llms.txt)