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Tanod utilpeek Sentiment Analysis
x402
Tanod utilpeek Sentiment Analysis is a paid API for AI agents from tanod.dev, paid per call via x402, $0.001/call, status unknown (last checked 2026-10-11).
Analyzes the sentiment of English text using VADER, returning a compound score and sentiment label (positive/negative/neutral).
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 VADER compound score (a float between -1.0 and 1.0) and a sentiment label (positive, negative, or neutral). If per_sentence is enabled, the response also includes individual scores and labels for each sentence in the input text.
Example use cases
Customer review sentiment triage
“Analyze the sentiment of this review and tell me the compound score and label: 'Absolutely loved the product — fast shipping, great quality, and the support team was incredibly helpful.'”
Social media tone monitoring
“I have this tweet: 'Can't believe how bad the service was today, waited 45 minutes and nobody even acknowledged me.' — can you run VADER sentiment on it and give me the score and label?”
Per-sentence feedback breakdown
“Run sentiment analysis on this paragraph of customer feedback sentence by sentence and give me the VADER score and label for each: 'Setup was easy and intuitive. However, the battery drains way too fast. The design looks premium though.'”
Technical details
Endpoint
POSThttps://tanod.dev/v1/text/sentiment?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need fast, rule-based English sentiment scoring via VADER with a simple compound score and label, especially useful for short texts like reviews, tweets, or comments. Prefer this over ML-based alternatives when you want deterministic, explainable scores without GPU latency. The per_sentence option is ideal when you need fine-grained sentiment broken down by sentence within a longer document.
Side effects
- Empty or whitespace-only text input rejected (minLength:1)
- Text exceeding 200,000 characters rejected
- Non-English text may produce inaccurate or unreliable sentiment scores since VADER is English-only
- Payment failure or insufficient USDC balance returns a 402 error
- Malformed JSON request body returns a 400 error
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| text | string | The text (at most 200,000 characters). |
| per_sentence | boolean | Also score each sentence (default false: whole text only). |
Output
Schema · Response
{
"type": "json",
"example": {
"label": "negative",
"method": "VADER lexicon and rules (heuristic; English only). Document score = mean of sentence scores. Labels: compound >= 0.05 positive, <= -0.05 negative. Misses sarcasm, domain jargon and non-English text; not a trained model.",
"source": {
"url": "https://github.com/cjhutto/vaderSentiment",
"library": "vaderSentiment 3.3.2",
"license": "MIT"
},
"neutral": 0.509,
"compound": -0.3716,
"language": "en",
"negative": 0.302,
"positive": 0.189,
"sentences": 1,
"input_truncated": false
}
}Tags
x402
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