translate-en
Translate text into natural, fluent English. input=text in any language. [x402: 0.003 USDC on Base, pay-per-use]
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| input | Yes | texto a traducir |
Translate text into natural, fluent English. input=text in any language. [x402: 0.003 USDC on Base, pay-per-use]
| Name | Required | Description | Default |
|---|---|---|---|
| input | Yes | texto a traducir |
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?
There are no annotations, so the description carries the full behavioral disclosure burden. It discloses the pay-per-use cost (0.003 USDC on Base), which is a meaningful and non-obvious behavioral detail. It does not mention output formatting or limits, but the operation is straightforward and non-destructive.
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 short and front-loaded with the main purpose. It includes only essential operation info and a billing note, though the 'input=text in any language' fragment is terse and could be stylistically improved.
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 one-parameter tool with no output schema or nested objects, the description covers the necessary context: source language, target language, and billing behavior. It lacks only minor details such as maximum text length or exact output format, which are not critical for a simple translation call.
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 100%, but the schema only describes the input as 'texto a traducir' in Spanish. The description adds meaning by specifying that the input can be text in any language and that output will be natural, fluent English, clarifying the language pair beyond the raw schema.
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 states a specific verb and resource: translate text into natural, fluent English. It clearly identifies the target language and distinguishes the tool from other translation-related siblings, though it does not explicitly name alternative tools.
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?
Usage context is implied: use when you need English translation of text from any source language. There is no explicit when-to-use/when-not-to-use or mention of alternatives such as translate-to, traducir, or translate-doc, so guidance is limited.
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.
The set contains many trivially indistinct tools: ai-inference/inference, compress/comprimir, count-tokens/contar-tokens, detect-language/language-detect, and multiple overlapping OCR receipt variants. With 160 tools and pairs that differ only by language or suffix, an agent cannot reliably distinguish several capabilities.
Most names are readable lower-hyphen identifiers, but they mix action verbs, noun phrases, domain prefixes, pipeline suffixes, Spanish/English, and arbitrary demo/batch labels. There is a loose convention, but no consistent verb_noun pattern.
160 tools on one server is an extreme count and clearly unwieldy. Even as a marketplace, exposing every variant, demo, and composed bundle as a top-level MCP tool overwhelms agent selection and adds little distinct capability.
The set covers a huge range of text, image, audio, code, market, compliance, and content-workflow tasks, so many intents have some available tool. However, it is a grab-bag rather than a defined service surface, and the arbitrary demo/specialized variants make it unclear whether a needed operation truly exists or is just a duplicate.