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add_translation_memory_entry

Store approved translations in a memory database to reuse them for future localization tasks, ensuring consistency across projects.

Instructions

Add a new entry to the translation memory.

Use this to store approved translations so they are reused in future localizations.

Args: source_text: The original text. target_text: The approved translation. source_language_code: Source language code (e.g. "en"). target_language_code: Target language code (e.g. "fr-FR"). name: Optional label for this entry (e.g. "homepage hero copy").

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
source_textYes
target_textYes
source_language_codeYes
target_language_codeYes
nameNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior3/5

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

No annotations are provided, so the description carries the full burden. It indicates this is a write operation ('Add'), but doesn't disclose behavioral traits like permissions needed, whether entries are immutable, rate limits, or error conditions. The description adds value by explaining the purpose of storing for reuse, but lacks operational details.

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?

The description is appropriately sized and well-structured: a brief purpose statement, usage guideline, then parameter explanations in a clear 'Args:' section. Every sentence earns its place, with no redundant or vague phrasing.

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

Completeness4/5

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

Given the tool has an output schema (which handles return values), no annotations, and 5 parameters with 0% schema coverage, the description does well by covering purpose, usage, and parameter semantics. However, it lacks behavioral context (e.g., mutation effects, error handling) that would be important for a write operation without annotations.

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

Parameters5/5

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

Schema description coverage is 0%, so the description must compensate. It provides clear semantics for all 5 parameters, explaining what each represents (e.g., 'source_text: The original text', 'target_text: The approved translation') with examples for language codes and the optional name. This adds significant meaning beyond the bare schema.

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?

The description clearly states the tool's purpose with specific verb ('Add') and resource ('new entry to the translation memory'), and distinguishes it from siblings by focusing on storage rather than retrieval or translation. The second sentence explains the functional goal ('store approved translations so they are reused'), making the purpose unambiguous.

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

Usage Guidelines5/5

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

The description explicitly states when to use this tool ('Use this to store approved translations so they are reused in future localizations'), providing clear context. It implicitly distinguishes from siblings like 'search_translation_memory' (for retrieval) and 'translate' (for translation), though it doesn't name alternatives directly.

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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