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Sentiment Analysis

SentimentAnalysis
Read-only

Sentiment Analysis: Sentiment Analysis evaluates the emotional tone of any text string. It returns an AFINN valence score, a comparative word-normalized score, and a five-tier sentiment label from very negative to very positive. Paid plans add sentence-level labels from a transformer model, a mixed-sentiment flag, magnitude, and the words that drove the score. (Cost: 10 credits)

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "text": "<text>"
    },
    "name": "SentimentAnalysis"
  }
}

Input Schema

NameRequiredDescriptionDefault
textYesThe text to analyze the sentiment of

Output Schema

NameRequiredDescriptionDefault
dataNo
errorNoError message, null on success
statusNo"ok" on success

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.9/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already cover the safety profile (readOnly, non-destructive), and the description adds genuinely useful behavior: the exact scoring model (AFINN), the output tiers, and the fact that richer output is gated behind a paid plan. It also discloses a 10-credit cost, which an agent needs before invoking. It stops short of describing failure modes or length limits.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Three sentences, front-loaded with the core purpose and followed by output shape and cost – nothing wasted. Minor redundancy in restating the tool name/title at the start of the first sentence.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

An output schema exists, yet the description still gives a helpful preview of returned fields, plus the plan-dependent differences and credit cost. For a one-parameter, low-complexity tool this is close to complete; only input limits and error behavior are unaddressed.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

There is a single parameter with 100% schema description coverage, so the schema already explains 'text'. The description says the tool accepts 'any text string', which adds only marginal meaning and no format, length, or encoding guidance.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb and resource: evaluates the emotional tone of a text string, and enumerates exactly what it produces (AFINN valence, comparative score, five-tier label). That distinguishes it clearly from nearby siblings such as TextReadabilityScore, ContentFilter, or ProfanityFilter.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Usage is only implied by 'any text string' – there is no explicit when-to-use statement, no mention of when a different tool (e.g. ContentFilter or TextSummarizer) would be preferable, and no constraints such as input length limits or language assumptions.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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