Tanod MCP server

utilpeek: spellcheck a text

text_spellcheck

Read-onlyIdempotent

Find misspelled words in text and get spelling suggestions with positions. Use it to check a document against English, Spanish, French, German, or Portuguese dictionaries.

Instructions

utilpeek: Spellcheck a text with suggestions. For counts and readability use text_stats. Returns the words not in the language's word-frequency dictionary (pyspellchecker), with positions (offset, line, column) and up to max_suggestions suggestions by edit distance; numbers, acronyms, CamelCase, URLs and emails are skipped. A dictionary lookup, not grammar or context aware. Typically under 1 s (up to 2 s on a language's first use). 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/spell-check-api.html

Input Schema

TableJSON Schema

Name	Required	Description	Default
`text`	Yes	The text (at most 200,000 characters).
`language`	No	Dictionary: en (default), es, fr, de or pt.	en
`max_suggestions`	No	Corrections suggested per misspelled word (0-10, default 5).

Output Schema

TableJSON Schema

Name	Required	Description
`note`	No
`error`	No	Only on an error result: an object {code, message}, or the reason string of an x402 PaymentRequired object.
`source`	No
`language`	No
`truncated`	No
`misspelled`	No
`words_checked`	No
`misspelled_unique`	No
`suggestions_incomplete`	No

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.3.0Oct 15, 2026

TDQS

A4.6/5.0

Behavior5/5

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

Annotations already cover read-only, idempotent, non-destructive, closed-world. The description adds substantial context beyond that: which tokens are skipped (numbers, acronyms, CamelCase, URLs, emails), latency (~1 s, up to 2 s on first language use), price, free-tier limits, and a no-logging/no-storage privacy 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, then alternative routing, then return shape, limits, latency, price, and privacy. Dense but nearly every clause earns its place; the pricing/docs tail is slightly heavy but still useful for a paid API.

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, yet the description still sketches the return payload (dictionary-miss words with offset/line/column and suggestions), and adds availability, cost, and privacy caveats an agent needs before calling. Nothing material 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 coverage is 100%, so all three parameters are already documented with defaults, ranges, and enum values. The description only restates that output includes up to max_suggestions suggestions by edit distance, adding marginal param-level meaning. Baseline 3 applies when the schema carries the load.

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 ('Spellcheck a text with suggestions') and immediately distinguishes itself from the sibling text_stats. An agent can tell exactly what this does versus the neighboring text tools 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?

Explicitly routes the agent: 'For counts and readability use text_stats,' and defines the negative boundary ('a dictionary lookup, not grammar or context aware'). This is a clear when-to-use and when-not-to-use statement.

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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