Sentiment Analysis for AI Agents

Overview

Sentiment Analysis works by analyzing the text and comparing it to a large database of sentiment analysis models. It uses advanced algorithms to analyze the sentiment of the text and returns the sentiment score and the sentiment label.

Live Test Sentiment Analysis for AI Agents Source →

The tool

Once your client is connected to the VerveContext server, this appears in its tool list as SentimentAnalysisforAIAgents. It is read-only and open-world — it fetches and never mutates anything on your side — so most clients call it without asking you to confirm.

Tool call

{
  "name": "SentimentAnalysisforAIAgents",
  "arguments": {
    "text": "I absolutely love this product! It exceeded all my expectations."
  }
}

You do not name the tool yourself; the model picks it. Asking about I absolutely love this product! It exceeded all my expectations. in the terms this source covers is enough for it to reach for SentimentAnalysisforAIAgents on its own — naming it explicitly also works, and is the way to force the call.

Connecting

One server URL covers every source in the catalog, including this one. Authorization is OAuth: the client opens a browser once, and there is no key to paste into a config file.

{
  "mcpServers": {
    "vervecontext": {
      "url": "https://api.vervecontext.com/v1/mcp"
    }
  }
}

Per-client setup — Claude, Cursor, VS Code, ChatGPT — is on the MCP setup page.

Arguments

These are the properties on the tool's inputSchema, so a well-behaved client validates them before the call is made. Premium arguments are accepted on every plan but only take effect on plans that include them.

Argument	Type	Description
`text`Required	string	The text to analyze the sentiment of length 0–50000

What the model gets back

The result carries a structuredContent object matching the tool's declared outputSchema, so a client reads fields without parsing prose. status is "ok" and error is null on success; a null field means the value was not available for that input, not that the call failed.

Result

{
  "status": "ok",
  "error": null,
  "data": {
    "comparative": 0.25,
    "sentimentText": "positive",
    "sentiment": 3,
    "isPositive": true,
    "isNegative": false,
    "normalizedScore": 0.3
  }
}

Response fields

Paths are relative to data. Premium fields are absent rather than zeroed on plans that do not include them, so check for presence instead of comparing to 0.

Field	Type	Example	Description
`comparative`	number	`0.25`	The sentiment score divided by the number of words, so long and short texts compare
`sentimentText`	string	`positive`	The score read as a label: very positive, positive, neutral, negative or very negative
`sentiment`	number	`3`	AFINN sentiment score: the summed valence of the matched words, negative for negative sentiment
`isPositive`	boolean	`true`	Whether the sentiment is positive
`isNegative`	boolean	`false`	Whether the sentiment is negative
`normalizedScore`Premium	number	`0.3`	Sentiment score normalized to -1 to 1 range

Why ground on it

A model can produce something that looks like this answer from its training data, and be confidently out of date or simply wrong. This source returns the current value in a shape you can check, which is the difference between an answer you can cite and one you have to hedge.

Point an evaluation at comparative: it is the field most worth pinning a claim to, and it is either present and current or absent — never plausibly invented.

Failure modes

Errors come back as tool errors carrying a sentence the model can act on, not a bare status code. Error handling covers the full list.

Status	What it means
`400` / `422`	The arguments did not validate. The message names the offending one.
`401`	The OAuth session is invalid or expired — reconnect the server.
`403`	Blocked by a key restriction or an IP allow-list. Never a bad identity.
`404`	This source is not part of VerveContext. Check the catalog.
`429`	Out of credits, or a brief rate limit. The message tells them apart.

A call costs 10 credits each time the tool actually runs; a model that reasons about the tool without calling it costs nothing.

Use cases

Support Ticket Escalation: Route angry customer messages to senior support reps immediately when the sentiment score drops to very negative.
Review Stream Filtering: Product managers categorize incoming app store feedback into positive praise and negative complaints using the five-tier sentiment label.
Public Mention Tracking: When tracking brand tags on social networks, marketing teams flag sudden spikes in negative valence scores to address PR issues early.
Survey Response Sorting: Sort open-ended employee survey responses automatically by comparative sentiment to isolate recurring workplace grievances without manual reading.

Other ways to use Sentiment Analysis for AI Agents

Set up Sentiment Analysis for AI Agents on VerveContext, or reach the same source a different way. Your VerveContext account and credits work on all of them — one key, one balance.

Call it as a REST APIOne HTTPS endpoint and an x-api-key header, with SDKs for Node, Python and .NET.APIVerve →Reference →

Give it to an AI agentConnect over MCP and your agent calls it as a native tool — Claude, Cursor, ChatGPT.VerveKit →Reference →

Google Sheets or ExcelA =VERVE() formula fills a column — no script, no export, recalculates in place.VerveSheets →Reference →