This server is a Korean-to-Japanese language coaching assistant that helps beginners translate, learn, and practice Japanese expressions.
Translate Korean (KakaoTalk-style) messages into natural Japanese — provides romanized pronunciation (Hepburn), Korean meaning, and expression breakdowns, with options to specify relationship (friend, coworker, manager, etc.) and tone (casual, polite, cute, apologetic, etc.)
Correct user-written Japanese sentences — analyzes and corrects Japanese (or romanized) sentences, explaining why they are unnatural in beginner-friendly terms
Explain Japanese expressions — gives detailed explanations including reading, romanization, meaning, usage context, and example sentences tailored to the learner's level
Create review flashcards — saves learned expressions as review cards with romanization, Korean meaning, difficulty rating, and example sentences for later study
Generate daily quizzes — produces quick quizzes (1–10 questions) based on saved review cards, with customizable focus type (meaning, pronunciation, expression, grammar, or mixed) and optional immediate answer reveal
Integrates with KakaoTalk to translate Korean messages into natural Japanese, providing romanization, expression breakdown, and tone alternatives based on relationship and learner level.
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@Harutalk Japanese Coach MCP Translate '오늘 늦어서 미안' to casual Japanese"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
한국어 카카오톡 문장을 자연스러운 일본어로 변환하고, 초급 학습자를 위한 발음·표현 설명·복습 카드·퀴즈를 제공하는 MCP 서버입니다.
Related MCP server: Greeting & Image Generation MCP Server
git clone https://github.com/your-repo/kakao-playmcp.git
cd kakao-playmcp
npm install
npm run buildexport ANTHROPIC_API_KEY=sk-ant-...claude_desktop_config.json에 추가:
{
"mcpServers": {
"harutalk-japanese-coach": {
"command": "node",
"args": ["/absolute/path/to/kakao-playmcp/dist/index.js"],
"env": {
"ANTHROPIC_API_KEY": "sk-ant-..."
}
}
}
}ANTHROPIC_API_KEY=sk-ant-... node dist/index.jstranslate_kakao_message한국어 문장을 자연스러운 일본어로 변환합니다. 로마자 발음(Hepburn), 핵심 표현 분해, 말투별 대안 표현을 함께 반환합니다.
파라미터 | 타입 | 기본값 | 설명 |
| string | 필수 | 번역할 한국어 문장 |
| enum |
| 상대방 관계 (friend/coworker/stranger 등) |
| enum |
| 원하는 말투 (casual/polite/cute 등) |
| enum |
| 학습자 수준 |
correct_japanese_sentence사용자가 작성한 일본어(또는 로마자) 문장을 교정하고 어색한 이유를 초급자 기준으로 설명합니다.
파라미터 | 타입 | 기본값 | 설명 |
| string | 필수 | 교정받을 문장 |
| string | - | 의도한 의미 (선택) |
| enum |
| 학습자 수준 |
explain_expression일본어 표현을 초급자가 이해하기 쉽게 설명합니다. 예문과 로마자 발음 포함.
파라미터 | 타입 | 기본값 | 설명 |
| string | 필수 | 설명받을 일본어 표현 |
| string | - | 표현의 사용 맥락 (선택) |
| enum |
| 학습자 수준 |
create_review_card표현을 복습 카드로 저장합니다. 저장된 카드는 퀴즈 생성에 활용됩니다.
파라미터 | 타입 | 설명 |
| string | 일본어 표현 |
| string | 로마자 발음 |
| string | 한국어 뜻 |
| enum | 난이도 (easy/normal/hard) |
generate_daily_quiz저장된 복습 카드로 1분 퀴즈를 생성합니다.
파라미터 | 타입 | 기본값 | 설명 |
| number |
| 문제 수 (1~10) |
| enum |
| 출제 유형 (meaning/pronunciation/expression/grammar/mixed) |
| enum |
| 학습자 수준 |
| boolean |
| 정답 즉시 표시 여부 |
모든 일본어 문장은 3단 구조로 반환됩니다:
今日ちょっと遅れそう。
(Kyou chotto okuresou.)
오늘 조금 늦을 것 같아.MIT
사용자가 직접 작성한 일본어 문장을 교정하고, 왜 어색한지 초급자 기준으로 설명합니다.
| Name | Required | Description | Default |
|---|---|---|---|
| tone | No | unknown | |
| learner_level | No | absolute_beginner | |
| user_sentence | Yes | 사용자가 작성한 일본어 문장 또는 로마자 문장 | |
| intended_meaning | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description bears full responsibility for behavioral disclosure. It states the tool corrects and explains awkwardness, but does not disclose important traits such as whether it modifies the sentence, requires authenticity, or any limitations (e.g., only works for Japanese, input constraints). The behavior is underspecified.
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 a single sentence that gets straight to the point: it corrects and explains. It is concise and front-loaded with the action. However, it omits important details that could be added without bloat, so it does not earn a 5.
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?
Given the lack of output schema and annotations, and four parameters (three optional) with unclear semantics, the description is insufficient. It does not explain what the output format is, how optional parameters affect behavior, or any prerequisites. A correction tool for Japanese sentences requires more context for reliable use.
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 only 25% (only user_sentence has a description). The tool's description adds little beyond restating user_sentence as a user-written Japanese sentence. It does not explain the roles of tone, learner_level, or intended_meaning parameters, leaving the agent to infer their impact from enum names alone.
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 verb (교정하다 - correct, 설명하다 - explain) and the resource (일본어 문장 - Japanese sentence). It specifies the audience (초급자 기준 - beginner level) and implies it is for self-written sentences. This distinguishes it from sibling tools like 'translate_kakao_message' or 'explain_expression'.
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?
The description implies usage for correcting user-written Japanese sentences, but does not provide explicit when-to-use or when-not-to-use guidance. There is no mention of alternatives or conditions that would make this tool more appropriate than siblings, such as 'explain_expression' for individual expressions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
배운 일본어 표현을 복습 카드로 저장합니다.
| Name | Required | Description | Default |
|---|---|---|---|
| context | No | ||
| reading | No | ||
| difficulty | No | normal | |
| expression | Yes | ||
| romanization | Yes | ||
| korean_meaning | Yes | ||
| example_sentence | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description must convey behavioral traits. It only states 'saves' without any details on side effects, authentication needs, or data persistence. This is insufficient.
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 a single, efficient sentence with no waste. It is front-loaded and directly states the purpose.
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?
Given 7 parameters (3 required) and no output schema, the description is incomplete. It lacks details on usage, parameters, and expected behavior, leaving significant gaps for the agent.
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 0%, so the description should compensate. However, it does not explain any parameter meaning, leaving agents to infer from parameter names alone.
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's function: saving learned Japanese expressions as review cards. It uses a specific verb ('saves') and resource ('review cards'), and is distinct from sibling tools like correcting sentences or explaining expressions.
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 guidance on when to use this tool versus alternatives. The description does not mention prerequisites, exclusions, or context for usage.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
일본어 표현을 초급 학습자 기준으로 설명합니다. 읽기, 로마자 발음, 뜻, 사용 상황, 예문을 제공합니다.
| Name | Required | Description | Default |
|---|---|---|---|
| context | No | ||
| expression | Yes | 설명을 원하는 일본어 표현 또는 문장 | |
| learner_level | No | absolute_beginner |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, and the description does not disclose behavioral traits like side effects, authentication needs, or rate limits. Assumes read-only but not stated.
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?
Single concise sentence in Korean. Front-loaded and free of fluff. Adequately sized for the tool's simplicity.
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?
Given no output schema, the description lists the main output components (reading, romanization, meaning, usage, examples). Missing details on response structure or error handling, but sufficient for its purpose.
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 33% (only 'expression' has a description). The tool description adds high-level output details but does not explain how parameters like 'context' or 'learner_level' affect the output. Does not compensate for low coverage.
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 it explains Japanese expressions for beginner learners, listing specific output elements (reading, romanization, meaning, usage, examples). This distinguishes it from siblings like correct_japanese_sentence or translate_kakao_message.
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 guidance on when to use this tool versus siblings. Does not specify context, prerequisites, or exclusions. Implicitly for explaining expressions, but lacks direct comparison with alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
저장된 복습 카드를 바탕으로 1분 안에 풀 수 있는 퀴즈를 생성합니다.
| Name | Required | Description | Default |
|---|---|---|---|
| focus | No | mixed | |
| level | No | absolute_beginner | |
| quiz_count | No | ||
| reveal_answers | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must carry the burden. It mentions the source (review cards) and time limit, but does not disclose how cards are selected, what happens if no cards exist, or how parameters affect behavior. Significant gaps remain.
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 a single concise sentence with no wasted words. It is front-loaded and efficient, though slightly too brief given the tool's complexity.
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?
Given 4 parameters, no output schema, and no annotations, the description is incomplete. It does not explain quiz format, parameter effects, or expected output, making it insufficient for reliable agent use.
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 0%; the description adds no meaning to any of the 4 parameters (focus, level, quiz_count, reveal_answers). It fails to explain their purpose, defaults, or options, leaving the agent without guidance.
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 identifies the verb '생성합니다' (generates) and resource '퀴즈' (quiz) based on saved review cards. It distinguishes from siblings by specifying the quiz generation focus and time constraint (1 minute), though it could be more explicit about quiz format.
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?
The description provides context (based on review cards, within 1 minute) but gives no explicit guidance on when to use this tool versus alternatives. It does not mention when not to use it or compare with sibling tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
한국어 카카오톡 문장을 자연스러운 일본어로 변환합니다. 초급 학습자를 위해 로마자 발음, 한국어 뜻, 핵심 표현 설명을 함께 제공합니다.
| Name | Required | Description | Default |
|---|---|---|---|
| tone | No | natural | |
| korean_text | Yes | 일본어로 바꾸고 싶은 한국어 문장 | |
| relationship | No | unknown | |
| learner_level | No | absolute_beginner | |
| include_romanization | No | ||
| include_expression_breakdown | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must cover behavioral traits. It states the tool provides romanization, meaning, and expression breakdown, but does not describe whether the operation is read-only, if it requires authentication, any rate limits, or what the output format looks like. Transparency is minimal.
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 a single sentence in Korean that effectively communicates the core purpose and key features. No redundant information; it is front-loaded and concise.
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?
Given the complexity (6 parameters, 3 enums, no output schema), the description lacks detail on output structure, the role of tone and relationship, and how the multiple outputs (translation, romanization, breakdown) are returned. It is insufficient for an agent to fully understand the tool's behavior.
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 low (17%), but the description adds meaning for learner_level (beginner), include_romanization, and include_expression_breakdown via the stated output components. However, the tone and relationship parameters are not explained, leaving ambiguity in how they affect translation.
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 converts Korean KakaoTalk sentences to natural Japanese, specifying the target output includes romanization, Korean meaning, and expression breakdown for beginners. This distinguishes it from sibling tools like correct_japanese_sentence (correction) or explain_expression (explanation of given expression).
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?
The description implies usage for beginners but does not explicitly state when to use this tool over siblings (e.g., for translation vs. correction). No exclusions or alternatives are mentioned; usage context is only implied by the tool name and description.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Tools have mostly distinct purposes: correcting sentences, saving cards, explaining expressions, generating quizzes, and translating from Korean. Minor potential overlap between 'correct_japanese_sentence' and 'explain_expression' as both involve explanations, but one is for user-written sentences and the other for given expressions.
All tool names follow a consistent verb_noun pattern with underscore_case: correct_japanese_sentence, create_review_card, explain_expression, generate_daily_quiz, translate_kakao_message.
With 5 tools, the server is well-scoped for a language coach. The count is neither too few to be useful nor too many to be overwhelming.
The tool set covers core learning activities: correction, review storage, explanation, quiz generation, and translation. Missing features like vocabulary lists or pronunciation, but the domain is adequately covered for a beginner-focused coach with Korean translation.
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