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Tanod mlpeek – Text Similarity Scoring
x402
Tanod mlpeek – Text Similarity Scoring is a paid API for AI agents from tanod.dev, paid per call via x402, $0.001/call, status unknown (last checked 2026-10-10).
Computes cosine similarity scores between pairs of text strings using embedding models (English or multilingual), supporting up to 50 pairs per call.
Price / call$0.001
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 the cosine similarity score (0–1) for each text pair, per-pair token counts, truncation flags, model metadata (id, alias, source, license), and aggregate usage stats (total pairs, total tokens, truncated count). For a single pair, also returns a top-level `similarity` field for convenience.
Example use cases
Duplicate FAQ detection
“I have two support FAQ entries — 'How do I reset my password?' and 'What are the steps to change my login credentials?' — can you check how semantically similar they are so I can decide if one is redundant?”
Multilingual content matching
“Tell me how similar these two texts are using the multilingual model: 'Bonjour, comment puis-je vous aider?' and 'Hello, how can I help you?' — I want a cosine similarity score.”
Batch candidate ranking
“I have a job description and 5 candidate summaries — can you score all 5 against the job description for semantic similarity so I can rank the best matches? Use the small-en model.”
Technical details
Endpoint
POSThttps://tanod.dev/v1/similarity?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need a lightweight, pay-per-call cosine similarity score without managing your own embedding infrastructure. It is especially useful for agents doing relevance ranking, duplicate detection, or semantic deduplication at low volume. The multilingual model option makes it suitable for cross-language comparisons. Prefer it over full vector database lookups when you just need a point similarity score for 1–50 pairs on demand.
Side effects
- Text exceeds 4,000 character limit per string — request rejected with validation error
- More than 50 pairs submitted — request rejected
- Both `a`/`b` and `pairs` omitted — no input to process
- Invalid model enum value — returns validation error
- Payment of 0.001 USDC not attached — 402 Payment Required response
- Text truncated silently at 512 tokens per segment — similarity may be lower than expected for long inputs
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| a | — | First text of a single pair (at most 4,000 characters). |
| b | — | Second text of a single pair. |
| model | string | small-en (English) or multilingual. |
| pairs | — | Alternative to a / b: 1-50 {a, b} pairs. Priced per pair, minimum USD 0.001 per call. |
Output
Schema · Response
{
"type": "json",
"example": {
"model": {
"id": "bge-small-en-v1.5",
"alias": "small-en",
"source": "BAAI/bge-small-en-v1.5",
"license": "MIT",
"revision": "5c38ec7c405ec4b44b94cc5a9bb96e735b38267a"
},
"usage": {
"pairs": 1,
"tokens": 22,
"truncated": 0
},
"metric": "cosine",
"results": [
{
"index": 0,
"tokens": [
9,
13
],
"truncated": false,
"similarity": 0.913619
}
],
"similarity": 0.913619,
"max_tokens_per_text": 512
}
}Tags
x402
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