Tanod mlpeek Text Embeddings

x402

Tanod mlpeek Text Embeddings 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).

Converts 1–64 texts into 384-dimensional float vectors using bge-small-en or multilingual-e5-small models, with support for query/passage retrieval prefixes and L2 normalization.

Price / call$0.001

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Success—

StatusUnknown

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 with an array of embedding objects (each containing index, token count, 384-dimensional float or base64-encoded vector, and truncation flag), plus model metadata (id, alias, source, license, revision), usage stats (texts, tokens, truncated), encoding format, dimensions (384), input_type, normalization status, and any prefix applied.

Example use cases

RAG pipeline document ingestion

“I need to vectorize these 10 passage chunks from my documentation so I can store them in my vector database — use the multilingual model with the passage prefix and return normalized float embeddings.”

Semantic search query encoding

“Encode this user search query into a 384-dim vector using the English model with query prefix so I can find the most similar documents in my Pinecone index: 'how do I reset my account password?'”

Multilingual product similarity

“Embed these product descriptions — some are in Spanish, French, and German — using the multilingual model so I can cluster them by semantic similarity.”

Technical details

Endpoint

POSThttps://tanod.dev/v1/embed?utm_source=zero.xyz

When to prefer

Choose this endpoint when you need lightweight, fast 384-dimensional embeddings for English (bge-small-en) or ~100-language (multilingual-e5-small) text with built-in retrieval prefix support (query/passage), pay-per-use pricing starting at $0.001 per call, and no need to manage model infrastructure. Ideal for RAG pipelines, semantic search, clustering, and similarity tasks where a compact 384-dim vector is sufficient and cost efficiency matters over larger models.

Side effects

Texts exceeding 8,000 characters are rejected at the input level
Texts longer than 512 tokens are silently truncated (reported via truncated flag)
Batches exceeding 64 texts are rejected
Empty strings or zero-length texts are rejected
Payment failure (x402) if insufficient USDC balance
Invalid model alias returns a validation error
Malformed JSON body returns a 400 error

Input

Schema · Request body

Field	Type	Description
model	string	small-en = BAAI/bge-small-en-v1.5 (English); multilingual = intfloat/multilingual-e5-small (~100 languages). 384 dimensions both.
texts	array	1-64 texts, each at most 8,000 characters; each is truncated at 512 tokens (reported per text). Priced per text, minimum USD 0.001 per call.
encoding	string	base64 = little-endian float32 bytes.
normalize	boolean	L2-normalise (cosine similarity = dot product).
input_type	string	Retrieval prefix: query / passage (e5: 'query: ' / 'passage: '; bge: its query instruction / nothing). none = symmetric use.

Output

Schema · Response

{
  "type": "json",
  "example": {
    "data": [
      {
        "index": 0,
        "tokens": 8,
        "embedding": [
          -0.017226,
          0.004483,
          0.019969,
          0.00569
        ],
        "truncated": false
      }
    ],
    "note": "float values rounded to 6 decimal places",
    "model": {
      "id": "bge-small-en-v1.5",
      "alias": "small-en",
      "source": "BAAI/bge-small-en-v1.5",
      "license": "MIT",
      "revision": "5c38ec7c405ec4b44b94cc5a9bb96e735b38267a"
    },
    "usage": {
      "texts": 1,
      "tokens": 8,
      "truncated": 0
    },
    "encoding": "float",
    "dimensions": 384,
    "input_type": "none",
    "normalized": true,
    "prefix_applied": null,
    "max_tokens_per_text": 512
  }
}

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