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Tanod MCP server

utilpeek: make a URL slug from text

make_slug
Read-onlyIdempotent

Convert text into a URL slug by removing accents, transliterating Greek and Cyrillic, and replacing spaces with separators. Cut to a max length for clean links.

Instructions

utilpeek: Make a URL slug from text. For other case styles use text_case. Returns a URL slug: accents removed, Latin ligatures, Greek and Cyrillic transliterated, apostrophes dropped and other characters turned into one separator, cut at a separator when over max_length. CJK, Arabic, Hebrew, Indic, Thai and emoji are not romanised: they are dropped and counted in dropped_characters. Nothing left to slug is a 422 empty_slug (not charged). Typically under 0.1 s. Price: USD 0.001. Free: 10 utilpeek calls per IP per UTC day. Docs: https://tanod.dev/learn/slug-generator-api.html

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
textYesThe text (at most 1,000 characters).
lowercaseNoLower-case the slug (default true).
separatorNo- (default) or _.-
max_lengthNoLongest slug (1-200, default 80); cut at a separator.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
slugNo
errorNoOnly on an error result: an object {code, message}, or the reason string of an x402 PaymentRequired object.
lengthNo
sourceNo
truncatedNo
transliteratedNo
dropped_charactersNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.3.0

TDQS

A4.4/5.0
Behavior5/5

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

Annotations already declare readOnly/idempotent/non-destructive, and the description layers on substantial extra behavior: transliteration and apostrophe handling, dropping of CJK/Arabic/Hebrew/Indic/Thai/emoji with a `dropped_characters` count, the 422 empty_slug error, latency, price, and a free daily quota.

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 purpose and the sibling alternative, then packs behavior, error mode, latency, price, quota, and docs into a compact block. Slightly dense for one paragraph, but nearly every clause carries operational value for a priced 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, so return values need not be explained, yet the description still flags the notable `dropped_characters` output and the 422 failure mode. Combined with pricing, timing, and quota details, nothing needed to call it 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 text, lowercase, separator, and max_length are all already documented with defaults and bounds. The description restates the separator/truncation behavior but adds no syntax or meaning beyond the schema, so the baseline 3 is correct.

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 ('Make a URL slug from text') and immediately distinguishes itself from the sibling text_case for other case styles. An agent can tell exactly what this tool produces without reading the schema.

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?

Explicitly routes non-slug case conversions to text_case, giving a clear alternative with the condition that selects it. It does not cover when-not-to-use cases beyond that (e.g. non-Latin text that will be dropped), so it stops short of full when/when-not coverage.

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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