Translate text between 75+ languages with auto-detection. Supports dynamic pricing based on text length.
| Name | Required | Description | Default |
|---|---|---|---|
| to | Yes | Target language code (e.g. 'es', 'de', 'ja') | |
| from | No | Source language code (e.g. 'en', 'fr'). Use 'auto' for auto-detection | |
| text | Yes | Text to translate |
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?
Beyond the readOnlyHint annotation, the description adds useful behavioral details: auto-detection support and dynamic pricing based on text length. These are meaningful and do not contradict the annotation.
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?
The description is two sentences, front-loaded with the core purpose, and contains no extraneous information. Every word adds value.
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 translation tool with full parameter documentation and a readOnlyHint, the description adequately covers features and contextual details. No output schema is needed, and the description is complete for the tool's complexity.
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 descriptions cover all three parameters with 100% coverage, but the description enhances this by explaining auto-detection, which directly relates to the 'from' parameter's 'auto' option, and hints at pricing implications tied to text length.
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?
The description clearly states 'Translate text between 75+ languages with auto-detection', using a specific verb and resource. It distinguishes this tool from siblings like ai_chat or currency by focusing on translation capabilities.
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?
The description gives clear context that this tool is for translation and highlights auto-detection, but it does not explicitly mention when not to use it or alternative tools. The context is evident, so it earns a 4 rather than a 5.
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.
Each tool targets a distinct capability: chat, embeddings, image generation, speech synthesis, transcription, currency exchange, translation, and weather. There is no overlap or ambiguity between them.
The five AI tools follow a consistent 'ai_' prefix pattern (ai_chat, ai_embeddings, etc.), but the three utility tools (currency, translate, weather) break this convention, creating a minor inconsistency.
With 8 tools, the server is well-scoped for a multi-purpose AI and utility toolkit. The count is neither too sparse nor overly heavy, and each tool has a clear role.
The toolset covers a broad range of AI modalities (text, embedding, image, audio) plus common utilities (currency, translation, weather). Minor gaps exist, such as video generation or web search, but these are not essential for the apparent scope.