基于 TypeScript 的模型上下文协议 (MCP) 服务器，用于促进开发者与 AI 之间的智能功能讨论。该服务器充当 AI 首席开发人员的角色，提供功能实现指导，维护讨论上下文，并帮助团队做出明智的架构决策。
该服务器提供：
关于功能实现和架构的互动讨论
持久记忆功能讨论和决策
对开发方法和最佳实践的智能指导
基于项目历史的上下文感知建议
参与有关功能需求的自然讨论
获得实施方法方面的专家指导
获得建筑建议
在多个讨论中保持上下文
功能讨论的持久存储
跟踪功能演变和决策
参考先前的讨论以获取背景信息
链接相关功能和依赖项
最佳实践建议
实施策略建议
架构模式建议
技术堆栈考虑因素
维护项目范围内的功能上下文
跟踪功能之间的依赖关系
商店架构决策
记住以前的讨论结果
Related MCP server: Context Management System
要与 Claude Desktop 一起使用，请添加服务器配置：
在 MacOS 上： ~/Library/Application Support/Claude/claude_desktop_config.json在 Windows 上： %APPDATA%/Claude/claude_desktop_config.json
{
"mcpServers": {
"feature-discussion": {
"command": "/path/to/feature-discussion/build/index.js"
}
}
}安装依赖项：
npm install构建服务器：
npm run build对于使用自动重建的开发：
npm run watch由于 MCP 服务器通过 stdio 进行通信，调试起来可能比较困难。我们推荐使用MCP Inspector ，它以包脚本的形式提供：
npm run inspector检查器将提供一个 URL 来访问浏览器中的调试工具。
欢迎大家贡献代码！请参阅我们的贡献指南，了解如何开始贡献，以及我们的行为准则，了解社区准则。
该项目根据 MIT 许可证获得许可 - 有关详细信息，请参阅LICENSE文件。
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.
AI colleagues that keep your standards, your project and their reasoning between sessions
Capture feature requests and bug reports from chat into a searchable, AI-categorized backlog.
Generate architecture docs, implementation plans, and dev tickets from your project requirements.
Bring your saved preferences, project decisions, and useful context into your next AI conversation.
