fast-translate
CONSUMER: translate + proofread to any language. input=text, to=lang. [x402: 0.05 USDC on Base, pay-per-use]
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| input | Yes | service input |
CONSUMER: translate + proofread to any language. input=text, to=lang. [x402: 0.05 USDC on Base, pay-per-use]
| Name | Required | Description | Default |
|---|---|---|---|
| input | Yes | service input |
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?
With no annotations, the description carries the full burden. It adds useful behavioral context such as the pay-per-use cost (0.05 USDC on Base) and the translate+proofread behavior. However, it does not describe the output format, payment prerequisites, auth requirements, or how the proofreading behaves, leaving important operational behavior undisclosed.
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 extremely tight and front-loaded, stating the action, inputs, and pricing in a single line. Every piece of information is useful, and there is no filler or repetition of the schema.
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 pay-per-use tool with only one input parameter and no output schema, the description is too incomplete. It does not explain how target language is expressed, what the response contains, or what prerequisites exist for payment on Base. An agent would likely need to guess or inspect sibling tools to call this correctly.
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?
The schema has only 'input' with the generic description 'service input'. The description says 'input=text, to=lang', which clarifies that input is text but introduces a 'to' parameter that does not exist in the schema. This leaves ambiguity about how the agent should actually specify the target language, so the added semantic value is undermined.
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 the tool translates and proofreads to any language, which gives a concrete verb and resource. It is not fully differentiated from sibling tools like translate-to or traducir, but the combined translate+proofread action narrows it well.
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 usage guidance is provided about when to choose this tool over translate-to, translate-doc, traducir, or proofread. The 'to any language' phrasing implies broad use, but the crowded sibling list makes the lack of explicit routing or exclusion criteria a clear gap.
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.