개발자와 AI 간의 지능적인 기능 논의를 지원하는 TypeScript 기반 모델 컨텍스트 프로토콜(MCP) 서버입니다. 이 서버는 AI 리드 개발자 역할을 수행하여 기능 구현에 대한 지침을 제공하고, 논의의 맥락을 유지하며, 팀이 정보에 기반한 아키텍처 결정을 내릴 수 있도록 지원합니다.
이 서버는 다음을 제공합니다.
기능 구현 및 아키텍처에 대한 대화형 토론
기능 토론 및 결정에 대한 지속적인 기억
개발 접근 방식 및 모범 사례에 대한 지능형 지침
프로젝트 내역을 기반으로 한 상황 인식 권장 사항
기능 요구 사항에 대한 자연스러운 토론에 참여하세요
구현 방법에 대한 전문가의 지침을 받으세요
건축 추천을 받으세요
여러 토론에서 맥락 유지
기능 토론의 영구 저장
기능 진화 및 결정 추적
맥락을 위해 이전 토론을 참조하세요.
관련 기능 및 종속성 연결
모범 사례 권장 사항
구현 전략 제안
아키텍처 패턴 권장 사항
기술 스택 고려 사항
프로젝트 전체 기능 컨텍스트 유지
기능 간 종속성 추적
매장 건축 결정
이전 토론 결과를 기억하세요
Related MCP server: Context Management System
Claude Desktop과 함께 사용하려면 서버 구성을 추가하세요.
MacOS의 경우: ~/Library/Application Support/Claude/claude_desktop_config.json Windows의 경우: %APPDATA%/Claude/claude_desktop_config.json
지엑스피1
종속성 설치:
npm install서버를 빌드하세요:
npm run build자동 재빌드를 사용한 개발의 경우:
npm run watchMCP 서버는 stdio를 통해 통신하므로 디버깅이 어려울 수 있습니다. 패키지 스크립트로 제공되는 MCP Inspector를 사용하는 것이 좋습니다.
npm run inspector검사기는 브라우저에서 디버깅 도구에 액세스할 수 있는 URL을 제공합니다.
기여를 환영합니다! 자세한 시작 방법은 기여 가이드라인을 , 커뮤니티 가이드라인은 행동 강령을 참조하세요.
이 프로젝트는 MIT 라이선스에 따라 라이선스가 부여되었습니다. 자세한 내용은 라이선스 파일을 참조하세요.
Start a new feature discussion
| Name | Required | Description | Default |
|---|---|---|---|
| title | Yes | Title or name of the feature |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden for behavioral disclosure. 'Start a new feature discussion' implies a creation/mutation operation, but it doesn't specify permissions needed, whether this is reversible, what happens after starting, or any rate limits. It lacks crucial context for safe and effective use.
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 zero wasted words. It's appropriately sized for a simple tool and front-loads the core purpose immediately.
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 annotations, no output schema, and a mutation tool with behavioral gaps, the description is incomplete. It doesn't explain what 'starting a feature discussion' entails, what the result looks like, or how it relates to the sibling tool. More context is needed for effective 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 100%, so the schema already documents the single 'title' parameter. The description adds no additional meaning about parameters beyond what the schema provides. Baseline 3 is appropriate when the schema does the heavy lifting.
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 'Start a new feature discussion' clearly indicates the action (start) and resource (feature discussion), but it's somewhat vague about what this entails. It doesn't specify what a 'feature discussion' is in this context or how it differs from the sibling tool 'provide_feature_input'.
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 no guidance on when to use this tool versus the sibling 'provide_feature_input'. There's no mention of prerequisites, context, or alternatives. The user must infer usage from the tool name alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Provide information for the current feature discussion prompt
| Name | Required | Description | Default |
|---|---|---|---|
| featureId | Yes | ID of the feature being discussed | |
| response | Yes | Your response to the current prompt |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions 'provide information', which suggests a read or input operation, but doesn't clarify if this is a mutation, requires specific permissions, has side effects, or details the response format. For a tool with zero annotation coverage, this is a significant gap in transparency.
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 that directly states the tool's purpose without unnecessary words. It is front-loaded and appropriately sized for its function, making it highly concise and well-structured.
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 annotations and output schema, the description is incomplete. It doesn't explain what the tool returns, how it interacts with the sibling tool 'begin_feature_discussion', or provide behavioral details. For a tool with two required parameters and no structured support, the description should offer more context to guide the agent effectively.
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 description coverage is 100%, with clear descriptions for both parameters ('featureId' and 'response'). The description adds no additional meaning beyond what the schema provides, such as explaining the context or format of 'response'. Given the high schema coverage, the baseline score of 3 is appropriate, as the schema handles the parameter documentation adequately.
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 states the tool's purpose as 'Provide information for the current feature discussion prompt', which is clear but vague. It specifies the action ('provide information') and context ('current feature discussion prompt'), but doesn't clarify what type of information or how it differs from the sibling tool 'begin_feature_discussion'. This makes it adequate but with room for improvement.
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 no guidance on when to use this tool versus alternatives, such as the sibling tool 'begin_feature_discussion'. It implies usage in the context of a 'current feature discussion prompt', but lacks explicit instructions on prerequisites, timing, or comparisons to other tools, leaving the agent with minimal direction.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
begin_feature_discussionprovide_feature_inputScored across 2 tools
The two tools have clearly distinct purposes: 'begin_feature_discussion' initiates a new discussion, while 'provide_feature_input' adds information to an existing one. There is no overlap in functionality, making it impossible for an agent to confuse them.
Both tools follow a consistent verb_noun pattern with snake_case naming. 'begin_feature_discussion' and 'provide_feature_input' use clear, descriptive verbs ('begin' and 'provide') paired with the same noun phrase ('feature_discussion' or 'feature_input'), ensuring predictability.
With only 2 tools, the server feels thin for a feature discussion domain. It lacks essential operations like retrieving, updating, or closing discussions, which limits its utility and suggests an incomplete scope.
The tool surface is severely incomplete for feature discussions. It covers starting a discussion and providing input, but missing critical operations such as getting discussion details, listing discussions, updating status, or adding comments, which will likely cause agent failures in real workflows.
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