Sentiment Analyzer X402 — independently scanned and version-tracked by SaferSkills.
SaferSkills independently audited Sentiment Analyzer X402 (Agent Skill) and scored it 100/100 (green). The audit ran 55 deterministic rules across Security, Supply Chain, Maintenance, Transparency, and Community; it found 0 high-severity and 0 lower-severity findings. The full rule-by-rule trace and per-finding evidence are below. Free, methodology-open.
Findings & checks · 0 flagged
Every scanned point with the score it earned and what moved between them.
First recorded scan — no prior version to compare against.
The primary manifest — the file an agent reads to learn what this artifact does.
Sentiment analysis with emotion detection, confidence scores, and key phrase extraction. Single or batch mode. Pay-per-call via x402 (USDC on Base L2) -- no API key, no signup, no rate-limit wall.
Part of the klymax402 marketplace -- 100 x402 micropayment APIs for AI agents, one wallet, USDC on Base.
Add to your MCP client config (Claude Desktop, Cursor, ElizaOS, etc.):
{
"mcpServers": {
"sentiment-analyzer": {
"url": "https://sentiment-analyzer.api.klymax402.com/mcp"
}
}
}curl -X POST "https://sentiment-analyzer.api.klymax402.com/api/analyze" \
-H "Content-Type: application/json" \
-d '{"text":"..."}'
# -> 402 Payment Required, with an x402 payment challenge in the response bodyAny x402-aware client (@x402/fetch, x402-agent-tools, ATXP) handles the 402 -> sign -> retry cycle automatically.
| Tool | Method | Path | Price | Description |
|---|---|---|---|---|
text_analyze_sentiment | POST | /api/analyze | $0.015 | Analyze sentiment of a single text |
text_analyze_sentiment_batch | POST | /api/analyze/batch | $0.10 | Analyze sentiment of up to 20 texts in batch |
text_analyze_sentimentUse this when you need to determine the emotional tone and sentiment of text. Returns structured sentiment analysis with emotion breakdown and key drivers.
Parameters
| Name | Type | Required | Description |
|---|---|---|---|
text | string | yes | The text to analyze for sentiment |
Returns
sentiment -- overall sentiment label (positive, negative, neutral)confidence -- confidence score 0-100emotions -- detected emotions with scores (joy, anger, fear, surprise, sadness)keyPhrases -- array of phrases driving the sentimentscore -- numeric sentiment score from -1.0 (negative) to 1.0 (positive)Example response:
{"sentiment":"positive","confidence":87,"score":0.73,"emotions":{"joy":0.82,"surprise":0.15,"anger":0.01,"fear":0.01,"sadness":0.01},"keyPhrases":["excellent results","exceeded expectations"]}When to use: responding to customer feedback, reviews, or social media mentions. Essential for brand monitoring, support ticket triage, and content tone analysis.
text_analyze_sentiment_batchUse this when you need to analyze sentiment of multiple texts at once (up to 20). Returns an array of individual sentiment results in one call.
Parameters
| Name | Type | Required | Description |
|---|---|---|---|
texts | array | yes | Array of texts to analyze (max 20) |
Returns
results -- array of sentiment objects, one per input textaverageSentiment -- overall average sentiment score across all textsdistribution -- count of positive/negative/neutral textsExample response:
{"results":[{"sentiment":"positive","confidence":91,"score":0.8},{"sentiment":"negative","confidence":74,"score":-0.6}],"averageSentiment":0.1,"distribution":{"positive":1,"negative":1,"neutral":0}}When to use: bulk analysis of reviews, survey responses, or social media feeds. Essential when comparing sentiment across multiple data points.
eip155:8453)100 x402 micropayment APIs for AI agents -- one wallet, USDC on Base, zero signup.
MIT
~30 seconds. Free. No account. Every finding cites a rule and a line of evidence.