Tanod MCP server

utilpeek: count word frequencies in a text

text_frequency

Read-onlyIdempotent

Count word and n-gram frequencies in text, drop stop words, and return the most frequent terms with counts and share. Use it to analyze word usage and repeated phrases.

Instructions

utilpeek: Count word and n-gram frequencies in a text. For scored keyphrases use text_keywords. Returns the most frequent words or 2-3 word n-grams (n-grams never cross punctuation) with count and share, dropping stop words (en, es, fr, de, pt, it, nl, or none), and optionally character counts. Typically under 0.3 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/keyword-extraction-api.html

Input Schema

TableJSON Schema

Name	Required	Description	Default
`text`	Yes	The text (at most 200,000 characters).
`ngram`	No	Term length in words: 1 (default), 2 or 3; n-grams never span punctuation.
`top_n`	No	Most frequent terms to return (1-1000, default 50).
`language`	No	Stop-word list to drop (none = keep every word).	en
`characters`	No	Also count characters (whitespace excluded).
`min_length`	No	Shortest word counted, in characters (1-50, default 1; single words only, ngram=1).
`case_sensitive`	No	Count "Word" and "word" apart (default false: terms are lowercased).
`include_numbers`	No	Also count numeric tokens such as 2026 (default false).
`remove_stopwords`	No	Drop the stop words of `language` (default true).

Output Schema

TableJSON Schema

Name	Required	Description
`top`	No
`error`	No	Only on an error result: an object {code, message}, or the reason string of an x402 PaymentRequired object.
`ngram`	No
`total`	No
`source`	No
`unique`	No
`language`	No
`characters`	No
`stopwords_removed`	No

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.3.0Oct 15, 2026

TDQS

A4.4/5.0

Behavior5/5

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

Annotations already cover readOnly/idempotent/non-destructive, and the description adds real load-bearing context beyond them: latency (~0.3 s), pricing and free-tier limits, and a privacy guarantee that submitted text is never logged or stored. These are exactly the traits an agent cannot infer from structured fields.

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-loads the core purpose and the sibling alternative, then layers output, latency, pricing and privacy without redundancy. The pricing/free-tier/docs boilerplate is compact but slightly dilutes the operational core.

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?

With an output schema present, return values need no explanation, and the description still covers scope, the keyphrase alternative, latency, cost and data handling. Nothing an agent needs to call this correctly is missing.

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 the schema already documents every parameter, making 3 the baseline. The description echoes the stop-word language list and the 'n-grams never cross punctuation' rule, which is a useful emphasis but adds little that the schema does not already state.

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 (Count) and resource (word and n-gram frequencies in a text), and explicitly distinguishes itself from the similar sibling text_keywords. An agent can route between text_frequency, text_keywords and text_stats without opening schemas.

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

Usage Guidelines4/5

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

Gives an explicit alternative ('For scored keyphrases use text_keywords'), which is genuine when-to-use routing. It does not mention text_stats or define exclusions beyond the keyphrase case, so it stops short of full 5-level guidance.

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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