Tanod MCP server

utilpeek: count words and score the readability of a text

text_stats

Read-onlyIdempotent

Count words, characters, sentences, and paragraphs, and score English readability with Flesch-Kincaid, Gunning fog, SMOG, Coleman-Liau, and ARI.

Instructions

utilpeek: Count words and score the readability of a text. For word frequencies use text_frequency. Returns counts (characters, letters, words, unique words, sentences, paragraphs, averages), reading and speaking time, and for English the Flesch reading ease, Flesch-Kincaid grade, Gunning fog, SMOG, Coleman-Liau and ARI scores (estimates from heuristic syllable counts; Dale-Chall is not offered). An unsupported lang is a 422 invalid_lang and text without words a 422 no_words (not charged). Word counts undercount scripts written without spaces. Typically under 0.2 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/text-statistics-api.html

Input Schema

TableJSON Schema

Name	Required	Description	Default
`lang`	No	ISO 639 code; readability scores are computed for English only (default en); any other language gets counts only.
`text`	Yes	The text (at most 200,000 characters).

Output Schema

TableJSON Schema

Name	Required	Description
`lang`	No
`note`	No
`error`	No	Only on an error result: an object {code, message}, or the reason string of an x402 PaymentRequired object.
`counts`	No
`source`	No
`syllables`	No
`readability`	No
`reading_time_seconds`	No
`speaking_time_seconds`	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?

Beyond the readOnly/idempotent annotations, it discloses concrete behavior: a 422 invalid_lang for unsupported languages, a 422 no_words (not charged) case, undercounting for space-less scripts, heuristic syllable estimates, latency (~0.2s), pricing, free-tier limits, and a no-logging/no-storage guarantee.

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 the sibling distinction, then packed with operational facts. Every clause carries information (errors, price, privacy), though the density is high and it reads as one long run-on rather than cleanly segmented sections.

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-param read tool with an output schema, the description is more than complete: it covers the return categories, error modes, language limits, cost, rate limits, and privacy, leaving an agent with nothing needed to call 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 coverage is 100% and the schema already documents both `lang` (ISO 639, English-only scores) and `text` (200,000 char cap). The description adds only marginal semantics, e.g., the 422 invalid_lang consequence and the heuristic-syllable caveat, so the baseline 3 holds.

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 specific verbs and resources: counting words and scoring readability. It explicitly names the sibling it is not for word frequencies and points to text_frequency, so the agent can route 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 one explicit alternative with its selecting condition ('For word frequencies use text_frequency'). Broader when-to-use context is implied by the readability-scoring scope rather than spelled out, and no exclusions beyond that are offered.

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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