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Title: Best Practices for Bilingual (EN/JA) Descriptions in mcp-go Tools
Context
I’m building MCP tools that will be consumed by LLMs (e.g. ChatGPT, Custom GPTs) via deep-search or OpenAI prompts. Each tool field has a description string, and I’m considering whether to include both English and Japanese:
My goal is to help the LLM correctly parse, explain, or render UI labels in either language without confusion.
Questions
Is it generally more effective to keep descriptions English-only, or to provide bilingual descriptions?
If bilingual is recommended, what formatting convention ensures the LLM cleanly separates the two languages?
Are there any pitfalls I should watch out for when mixing or prefixing languages?
Example of an “optimal” bilingual description format
Below is a generic template (not tied to any existing tool) demonstrating how I imagine separating the two languages clearly:
// Description for a hypothetical “user_id” fielddescription:= `
English:
Unique identifier for the user. Must be a positive integer.
Example: { "user_id": 1234 }
日本語:
ユーザーを一意に識別する番号です。正の整数で指定してください。
例: { "user_id": 1234 }
The “English:” and “日本語:” prefixes mark each block.
Blank lines separate the language blocks.
No mixing of EN/JP within the same sentence.
Would this style be parsed reliably by LLM-based tooling? Or is there a more robust pattern I should adopt?
Looking forward to your insights!
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Title: Best Practices for Bilingual (EN/JA) Descriptions in mcp-go Tools
Context
I’m building MCP tools that will be consumed by LLMs (e.g. ChatGPT, Custom GPTs) via deep-search or OpenAI prompts. Each tool field has a
description
string, and I’m considering whether to include both English and Japanese:My goal is to help the LLM correctly parse, explain, or render UI labels in either language without confusion.
Questions
Example of an “optimal” bilingual description format
Below is a generic template (not tied to any existing tool) demonstrating how I imagine separating the two languages clearly:
Would this style be parsed reliably by LLM-based tooling? Or is there a more robust pattern I should adopt?
Looking forward to your insights!
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