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Sentiment Trail MCP
x402
Sentiment Trail MCP is a paid API for AI agents from api.m2mcent.com, paid per call via x402, $0.05/call, status unknown (last checked 2026-10-12).
Analyzes text input to detect and trace sentiment patterns, returning sentiment classification and trail data
Price / call$0.05
Success—
StatusUnknown
How this service works
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Output
Returns a sentiment classification (positive, negative, neutral, or mixed) along with a sentiment trail showing how sentiment is distributed or progresses through the input text, potentially including confidence scores and polarity indicators.
Example use cases
Customer review sentiment scoring
“Analyze the sentiment of this product review for me: 'Absolutely love this laptop — fast, sleek, and the battery life is phenomenal. Only downside is the keyboard feels a bit mushy.' Tell me if it's overall positive, negative, or mixed.”
Social media post tone check
“What's the sentiment of this tweet? 'Can't believe how bad the traffic is today. Already 30 minutes late and the day hasn't even started. At least my coffee is good I guess.' Is it mostly negative or mixed?”
Support ticket emotional triage
“Run sentiment analysis on this support ticket message so I can prioritize it: 'I've been waiting three weeks for a refund and nobody has responded to my emails. This is completely unacceptable and I'm extremely frustrated.'”
Technical details
Endpoint
POSThttps://api.m2mcent.com/sentiment-trail-mcp/api/process?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need to trace sentiment across a body of text (not just a single label), such as detecting shifts in tone, evaluating mixed-sentiment passages, or when you need structured sentiment trail output rather than a simple polarity score. Useful for customer feedback pipelines, content moderation, or nuanced review analysis.
Side effects
- Empty or missing 'data' field returns a validation error
- Extremely long text inputs may be truncated or rejected
- Non-textual binary data will produce unreliable or error results
- Ambiguous or very short inputs may yield low-confidence classifications
Output
Schema · Response
{
"type": "json",
"example": {
"success": true
}
}Tags
x402
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