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translate

Translate text across 200+ languages using a local model. Runs offline on your CPU, ensuring data never leaves your machine.

Instructions

Translate text between 200+ languages using the NLLB-200 model, running entirely on your local CPU. No cloud APIs, no data leaves your machine.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
textYesThe text to translate
source_langYesSource language code (FLORES-200, ISO 639-1, or language name). Examples: "eng_Latn", "en", "English", "zho_Hans", "zh", "中文". Use list_languages to see all supported codes.
target_langYesTarget language code (FLORES-200, ISO 639-1, or language name). Examples: "zho_Hans", "zh", "中文", "eng_Latn", "en", "English".
Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Description highlights local CPU execution and no data leaving, but lacks details on limitations, speed, error handling, or output format. Without annotations, more behavioral context would be beneficial.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Two sentences front-loaded with the key action and key differentiator (local, private). No filler or redundant information.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

No output schema and no description of return values or structure. Critical omission for a transformation tool. Missing guidance on unsupported language codes or errors.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema covers all three parameters with 100% coverage. Description adds model context but no parameter-specific semantics beyond the schema. Baseline score is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

Description clearly states the verb 'translate', the resource 'text', and specifies the model (NLLB-200) and local execution. It distinguishes from sibling 'list_languages' by function.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No explicit guidance in the description about when to use this tool versus alternatives. The schema parameter description mentions list_languages for codes, but that is not part of the main description.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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