This is a paid sentiment analysis service that runs at $402 USDC and connects via SSE transport to a remote API endpoint. The repository itself is sparse, just configuration files and workflow setup, so you're essentially buying access to a hosted NLP service rather than running something locally. It promises sentiment scoring, emotion detection, confidence metrics, and key phrase extraction, which suggests it's wrapping a commercial text analysis API. You'd reach for this if you need Claude to evaluate customer feedback, social media posts, or survey responses and you're willing to pay for the convenience of not managing your own NLP infrastructure. The x402 pricing model is unusual for MCP servers, most of which are free and open source.
Public tool metadata for what this MCP can expose to an agent.
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. 1. sentiment: overall sentiment label (posi1 paramsUse this when you need to determine the emotional tone and sentiment of text. Returns structured sentiment analysis with emotion breakdown and key drivers. 1. sentiment: overall sentiment label (posi
textstringtext_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. 1. results: array of sentiment objects, one per input t1 paramsUse 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. 1. results: array of sentiment objects, one per input t
textsarraySentiment 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 body
Any 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 | $0.005 | Analyze sentiment of a single text |
text_analyze_sentiment_batch | POST | /api/analyze/batch | $0.04 | 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