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Romanize Latgalian to clean ASCII

latgalian_romanize
Read-onlyIdempotent

Strip Latgalian orthography down to clean, pronounceable ASCII for an English-trained downstream (a voice/TTS, a search box). See the text parameter doc for the exact letter mappings. Returns Markdown plus the romanized output as structuredContent matching the declared outputSchema. Pure local transform: no dictionary lookup, no network, and the output is always ASCII.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
textYesLatgalian text to reduce to clean, pronounceable ASCII. Lowercases and maps each special letter to an English-readable spelling: the macron vowels double (ā→aa, ē→ee, ī→ee, ō→o, ū→oo), the caron consonants respell (č→ch, š→sh, ž→zh), and the soft consonants take a 'y' glide (ļ→ly, ņ→ny, ķ→ky, ģ→gy; ŗ→r). A one-way fold — diacritics are dropped, never added. Pass a word, phrase, or several lines; whitespace and punctuation are preserved.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
textYesThe romanized ASCII output.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A3.9/5.0
Behavior4/5

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

Annotations already declare readOnly/idempotent/non-destructive and closed-world, so safety is covered. The description adds genuine behavioral context beyond that: it is a pure local transform, performs no dictionary lookup or network access, and always emits ASCII. It does not detail failure behavior on empty or unsupported input.

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?

Four short sentences, front-loaded with the purpose before the mechanics. Each sentence carries information (purpose, mapping pointer, return shape, execution profile), though the parenthetical examples are slightly redundant phrasing rather than new content.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

An output schema exists, so the brief note about Markdown plus structuredContent is sufficient and return values need no elaboration. For a single-parameter, local, read-only transform the description covers purpose, execution profile, and output shape adequately; only edge-case behavior is left unstated.

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% and there is a single parameter, so the schema itself carries the semantics (including the full letter-mapping table). The description usefully points the agent to the `text` parameter doc, but adds no mapping detail of its own beyond what the schema already states.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description names a specific verb and resource (romanize Latgalian orthography to ASCII) and states the downstream consumer (TTS, search box), which is unusually concrete. It doesn't explicitly name or contrast with the sibling latgalian_deromanize, so the direction is only inferred from the verb. Clear, but short of full sibling differentiation.

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?

It gives a clear use case — converting Latgalian for an English-trained downstream such as voice/TTS or a search box — which tells the agent when this tool is appropriate. It stops short of stating exclusions or explicitly routing the agent to latgalian_deromanize for the inverse operation.

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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