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utilpeek: extract keyphrases from a text (RAKE)

text_keywords
Read-onlyIdempotent

utilpeek: Extract the keywords and keyphrases of a text. For raw frequencies use text_frequency; for names and places use extract_entities. Returns the top keyphrases of a text by RAKE (phrases between stop words and punctuation, scored by word degree / frequency), with score and count. Typically under 0.3 s. Price: USD 0.002. Free: 10 utilpeek calls per IP per UTC day. The submitted text is never logged or stored. Docs: https://tanod.dev/learn/keyword-extraction-api.html

Input Schema

NameRequiredDescriptionDefault
textYesThe text (at most 200,000 characters).
top_nNoKeyphrases to return, best first (1-100, default 10).
languageNoStop-word language: de, en (default), es, fr, it, nl or pt.en
max_wordsNoLongest keyphrase, in words.

Output Schema

NameRequiredDescriptionDefault
noteNo
errorNoOnly on an error result: an object {code, message}, or the reason string of an x402 PaymentRequired object.
methodNo
sourceNo
keywordsNo
languageNo
candidatesNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed3 schema fields changed
    • addedInput schema / properties / language / description
      Added value: +"Stop-word language: de, en (default), es, fr, it, nl or pt."
    • addedInput schema / properties / top_n / description
      Added value: +"Keyphrases to return, best first (1-100, default 10)."
    • changedOutput schema / (root)
      Previous value: -nullNew value: +{
+  "properties": {
+    "candidates": {
+      "type": [
+        "integer",
+        "null"
+      ]
+    },
+    "error": {
+      "description": "Only on an error result: an object {code, message}, or the reason string of an x402 PaymentRequired object."
+    },
+    "keywords": {
+      "items": {
+        "properties": {
+          "count": {
+            "type": [
+              "integer",
+              "null"
+            ]
+          },
+          "phrase": {
+            "type": [
+              "string",
+              "null"
+            ]
+          },
+          "score": {
+            "type": [
+              "number",
+              "null"
+            ]
+          }
+        },
+        "type": [
+          "object",
+          "null"
+        ]
+      },
+      "type": [
+        "array",
+        "null"
+      ]
+    },
+    "language": {
+      "type": [
+        "string",
+        "null"
+      ]
+    },
+    "method": {
+      "type": [
+        "string",
+        "null"
+      ]
+    },
+    "note": {
+      "type": [
+        "string",
+        "null"
+      ]
+    },
+    "source": {
+      "type": [
+        "object",
+        "null"
+      ]
+    }
+  },
+  "type": "object"
+}
  2. Added

TDQS

A4.2/5.0
Behavior3/5

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

Annotations already declare readOnlyHint, idempotentHint and destructiveHint=false, so the safety profile is covered. The description still contributes real behavioral context beyond them: the RAKE scoring method, the sub-0.3 s latency expectation, the per-call price and free daily quota, and the privacy guarantee that submitted text is never logged or stored.

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 and disambiguation, then layered with algorithm, performance, cost and privacy in one dense block. Every sentence earns its place for an agent deciding whether to spend a call, though the price/quota/docs tail makes it longer than strictly minimal.

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?

An output schema exists, so return values need not be explained here, and the description still notes the 'score and count' fields. Combined with cost, latency and privacy disclosures, an agent has everything needed to select and invoke it correctly.

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?

Schema description coverage is 100%, so all four parameters (text, top_n, language, max_words) are already documented with ranges, defaults and enum values. The description's mention of phrases 'between stop words and punctuation' hints at why the language parameter exists but adds no syntax or constraint detail beyond the schema. Baseline 3 applies.

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 ('Extract the keywords and keyphrases of a text') and immediately distinguishes itself from the two nearest siblings, text_frequency and extract_entities. The RAKE gloss clarifies what kind of keyphrases come back.

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?

Explicitly routes the agent: raw frequencies go to text_frequency, names and places go to extract_entities. That is a concrete when-to-use-this vs when-to-use-alternatives statement, not merely implied context.

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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