get_language_optionsReturn the 38 supported Poly-Glot UI, input, and AI output languages. Language selection never changes entitlement.
| Name | Required | Description | Default |
|---|---|---|---|
| uiLanguage | No | EN |
| Name | Required | Description | Default |
|---|---|---|---|
| view | Yes | ||
| languages | Yes | ||
| localization | Yes |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate read-only and non-destructive; the description adds meaningful context: the exact count (38), the three language categories, and the guarantee that language selection never changes entitlement. This goes beyond the structured annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two short sentences; the first states the exact output, the second adds a behavior guarantee. No filler or redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple read-only options tool with an output schema, the description covers what the tool returns and an important side-effect guarantee. It lacks usage context and uiLanguage semantics, but these are minor for this low-complexity tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0% and the description never mentions the uiLanguage parameter, its default, or how it affects output. With no enum or description in the schema, the agent is left without any semantic guidance for the only parameter.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Description names a specific verb ('Return'), a precise resource (the 38 supported Poly-Glot UI, input, and AI output languages), and even a cardinality. This is clearly differentiated from sibling language tools like detect_language/translate_text by its 'options list' scope.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No when-to-use, prerequisites, or alternatives are given. The description implies this is for fetching supported language options, but it never says when to choose it over detect_language, translate_text, or localize_text.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Most tools are clearly separated by resource and action, but translate_text/localize_text and prepare_compare/prepare_custom_compare have adjacent purposes that could cause an agent to choose one when the other is intended. Descriptions provide enough distinction for a careful model, so the ambiguity is limited.
All 15 tools follow a consistent snake_case verb_noun pattern with verbs like build, detect, get, localize, open, prepare, run, search, transcribe, translate, and validate. There is no mixing of camelCase, vague imperative fragments, or generic action names.
15 tools is at the upper edge of a well-scoped set, and each functional area has dedicated tools. It is slightly heavy because a few pairs like translate/localize and prepare_compare/prepare_custom_compare are close variations, but the count is still reasonable for the server's broad workspace scope.
Core workflows are covered: template search/get/fill, translation and localization, custom model capabilities/validation/run, compare plan preparation, transcription, and entitlement checks. Gaps include no compare-plan execution tool, no template create/update/delete lifecycle, and no persistence for custom model configurations, but agents can work around these.