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Related Servers

Alternatives to verbaly

  • A
    license
    B
    quality
    D
    maintenance
    MCP server for managing translation projects in Weblate. With the ability to search, read and write transltaions.
    20
    63 npm
    24
    MIT
  • A
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    An MCP server that wraps the Tolgee localization platform API, enabling LLMs to manage translation projects, keys, translations, languages, and related workflows.
    27
    MIT
  • F
    license
    Not graded
    quality
    C
    maintenance
    Enables MCP-aware clients to manage inlang i18n catalogs, including discovering projects, parsing catalogs, handling keys, locales, variants, plurals, validating translations, and compiling messages, without parsing catalog files themselves.
    -

Related Servers

  • A
    license
    Not graded
    quality
    A
    maintenance
    Lets a coding agent find, translate, validate and review an app's existing localization files (Android strings.xml, Apple String Catalogs, gettext) directly in the repo, supplying code usage, comments, glossary and voice notes as context. Placeholders, plural forms, escaping and markup are checked in code before any translation is written, and results land as reviewable drafts that never overwrite hand edits or approved work.
    72 npm
    1
    MIT
  • A
    license
    Not graded
    quality
    C
    maintenance
    Enables AI agents, developers, and CI pipelines to run deterministic, offline localization QA — pseudolocalization, locale linting for separators/dates/placeholders/RTL, and glossary enforcement across 20 locales — without any network access or API key. When an OpenAI-compatible endpoint is configured, it can also translate into multiple locales and lint the results, or otherwise emit structured translation jobs instead of fabricating translations.
    3 npm
    MIT
  • A
    license
    A
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    AI localization from your editor — translate an app's string files into 46 languages with placeholder-safe, reproducible output. Eleven formats (JSON, .arb, .po, .strings, Android XML and more), and every translation is read back and checked before it lands.
    7
    760 PyPI
    MIT

TDQS

A4/5.0

Scored across 9 tools

Disambiguation4/5

Each tool maps to a distinct CLI operation (init, doctor, wrap, extract, status, missing, translate, write_drafts, drafts) with clear boundaries, and the descriptions explicitly orient the agent (doctor first, init when doctor says no config). The one soft spot is drafts: pending-review listings are surfaced in verbaly_status, optionally in verbaly_missing, and again in verbaly_drafts, so an agent may pick the wrong one for that question.

Naming Consistency4/5

Every tool uses the same verbaly_ snake_case prefix with a single short token, which is highly predictable and mirrors the CLI commands. Minor deviation: the tokens mix verbs (init, wrap, extract, translate) with nouns/adjectives (status, missing, doctor, drafts), so the suffix does not consistently signal an action.

Tool Count5/5

Nine tools is well within the ideal 3-15 range and each corresponds to a real stage of the localization workflow with no filler. Nothing appears redundant by count alone.

Completeness4/5

The surface covers onboarding (init, wrap), maintenance (extract, translate, write_drafts), and inspection (doctor, status, missing, drafts), with read/write variants and dryRun/overwrite flags. Gaps are minor: approving drafts and locale management are deliberately excluded (human-only), and pruning exists only as an extract flag rather than a standalone operation.

Maintenance

ActivityActive
ResponsivenessNo issues