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utilpeek: score the sentiment of a text (VADER)

text_sentiment
Read-onlyIdempotent

utilpeek: Analyze the sentiment of an English text. For your own labels use classify_zero_shot; for counts use text_stats. Returns VADER sentiment of an English text: compound (-1 to 1), positive / neutral / negative shares and a label (positive at 0.05 or more, negative at -0.05 or less), optionally per sentence. A lexicon heuristic: it misses sarcasm and domain jargon. Typically under 1 s. Price: USD 0.001. Free: 10 utilpeek calls per IP per UTC day. The submitted text is never logged or stored. Docs: https://tanod.dev/learn/sentiment-analysis-api.html

Input Schema

NameRequiredDescriptionDefault
textYesThe text (at most 200,000 characters).
per_sentenceNoAlso score each sentence (default false: whole text only).

Output Schema

NameRequiredDescriptionDefault
errorNoOnly on an error result: an object {code, message}, or the reason string of an x402 PaymentRequired object.
labelNo
methodNo
sourceNo
neutralNo
compoundNo
languageNo
negativeNo
positiveNo
sentencesNo
input_truncatedNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changed
    • addedInput schema / properties / per_sentence / description
      Added value: +"Also score each sentence (default false: whole text only)."
    • changedOutput schema / (root)
      Previous value: -nullNew value: +{
+  "properties": {
+    "compound": {
+      "type": [
+        "number",
+        "null"
+      ]
+    },
+    "error": {
+      "description": "Only on an error result: an object {code, message}, or the reason string of an x402 PaymentRequired object."
+    },
+    "input_truncated": {
+      "type": [
+        "boolean",
+        "null"
+      ]
+    },
+    "label": {
+      "enum": [
+        "positive",
+        "neutral",
+        "negative",
+        null
+      ],
+      "type": [
+        "string",
+        "null"
+      ]
+    },
+    "language": {
+      "type": [
+        "string",
+        "null"
+      ]
+    },
+    "method": {
+      "type": [
+        "string",
+        "null"
+      ]
+    },
+    "negative": {
+      "type": [
+        "number",
+        "null"
+      ]
+    },
+    "neutral": {
+      "type": [
+        "number",
+        "null"
+      ]
+    },
+    "positive": {
+      "type": [
+        "number",
+        "null"
+      ]
+    },
+    "sentences": {
+      "type": [
+        "integer",
+        "array",
+        "null"
+      ]
+    },
+    "source": {
+      "type": [
+        "object",
+        "null"
+      ]
+    }
+  },
+  "type": "object"
+}
  2. Added

TDQS

A4.8/5.0
Behavior5/5

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

Goes well beyond the annotations: it discloses the VADER lexicon-heuristic limitation (misses sarcasm and domain jargon), expected latency (under 1 s), cost (USD 0.001), a free-tier rate limit (10 calls per IP per UTC day), and a privacy guarantee ('never logged or stored'). These are exactly the operational traits annotations cannot convey.

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?

Front-loaded with purpose, then alternatives, then output shape, then caveats/limits. Every sentence carries information, though it is denser than strictly needed and re-states the 'utilpeek' prefix already in the title.

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

Completeness5/5

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

For a two-parameter read-only scoring tool with an output schema and full annotation coverage, the description adds limitations, latency, cost, rate limits, privacy, and a docs link. Nothing an agent needs before calling it is missing.

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

Parameters4/5

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

Schema coverage is 100%, so the baseline is 3, but the description adds meaning: it pins the scope to English input and explains that per-sentence scoring is optional output shaping. The VADER thresholds (positive ≥ 0.05, negative ≤ -0.05) describe the returned label rather than the parameters, so the gain is real but modest.

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 ('Analyze the sentiment of an English text') and explicitly disambiguates from the two nearest siblings ('For your own labels use classify_zero_shot; for counts use text_stats'). An agent can pick this tool over classify_zero_shot or text_stats without opening any schema.

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

Usage Guidelines5/5

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

Names both alternative tools and the exact condition that selects each (custom labels vs. counts). It also scopes applicability by language ('English text') and states the underlying method, so the agent knows when results are appropriate.

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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