为 Cursor 打造的确定性架构引擎与逻辑死锁协议
当项目复杂度提升时，AI 编程往往会陷入“幻觉螺旋”。VibeLogic MCP 并不是另一个代码生成器，而是一个处于“人类意图”与“代码执行”之间的管控层。它强制 AI 在动刀改代码前，先通过可视化蓝图进行审计。
可视化架构审计 (Visual Audit)：强制 AI 提取项目拓扑，通过 Mermaid 蓝图对齐逻辑，确保 100% 意图确定性。
逻辑死锁协议 (Security Lock)：独家封装的注意力劫持协议，强制 AI 在“图纸确认”前保持原地待命，阻断越权重构。
跨文件深度追踪：自动识别多文件关联变更，生成结构化的施工清单 (Action Plan)。
资产化存档：所有架构决策自动导出为 .md 文件，永久沉淀为项目的架构演进日志。
Related MCP server: mcp-edit-math
确保你已安装 Node.js，然后在 Cursor 中直接运行：
npx vibelogic-mcp打开 Cursor Settings -> Features -> MCP。 点击 + Add New MCP Server。 设置如下：
{
"mcpServers": {
"VibeLogic": {
"command": "npx.cmd",
"args": [
"-y",
"vibelogic-mcp@latest"
]
}
}
}"帮我分析下这个功能的逻辑，我想看图。" "我要重构登录模块的校验流程，先出图和施工单。" "基于当前代码，审计一下新增 API 对现有架构的影响。"
BYOK (自带 Key)：VibeLogic 运行在你的本地环境，不存储任何代码，完全复用你 Cursor 选定的模型能力。 拒绝复杂化：仅在涉及核心逻辑变动时激活，不干扰常规的 UI 调整和 Bug 修复。
仅在涉及项目核心逻辑变更、新增功能、跨文件流程修改，或用户明确要求“查看项目逻辑”、“查看当前项目实现”时调用。 警告：对于调整简单参数、常数、阈值、纯 UI 颜色/样式微调、补充注释、变量重命名、局部语法 Bug 修复等不涉及核心逻辑变动的操作，严禁调用此工具。
| Name | Required | Description | Default |
|---|---|---|---|
| intent | Yes | 用户意图：'view' 表示仅查看，'modify' 表示重构。 |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. It does not describe what the tool does (e.g., whether it reads or modifies, side effects, return values). It only states when to call, missing essential behavioral context.
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?
Description is concise and front-loaded with usage conditions. While slightly verbose with the warning, it remains efficient 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 no output schema, the description should explain return values or effect of the tool. It only covers when to call, leaving the agent without knowledge of what to expect from invocation.
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 coverage is 100% with a single parameter 'intent' that has an enum and description. The description adds no additional meaning beyond the schema, so baseline score of 3 is appropriate.
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 is for core logic changes, new features, cross-file modifications, or when user asks to view project logic. It distinguishes what is not its purpose (trivial changes) effectively, despite having no sibling tools to differentiate from.
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?
Explicitly specifies when to call and when not to call, with concrete examples of allowed and forbidden use cases. This provides strong guidance for an AI agent to select the appropriate tool.
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.
v0.1.0get_logic_blueprintScored across 1 tool
With only one tool, there is no risk of confusion between tools.
The single tool uses a verb_noun pattern (get_logic_blueprint), and with only one tool, consistency is not an issue.
One tool is too few for a server named 'vibelogic', which suggests a need for multiple logic-related operations. The tool is also highly restricted in usage, further limiting its utility.
A single tool cannot cover the expected lifecycle or operations for logic manipulation. The tool itself warns against many common tasks, leaving obvious gaps.
Deterministic AI code review, with an audit record. Governance inside the agent loop.
Let Claude, Cursor, or ChatGPT author Mermaid diagrams your team can read and share.
AI Agent with Architectural Memory. Impact analysis (free), tests and code from the graph (pro).
Pre-execution governance for AI agents. Deterministic PASS/FAIL/REVIEW verdicts, replayable proof.
