LORE 是一个用于 TypeScript 项目的基于插件的代码考古引擎。它通过模型上下文协议 (MCP) 解析你的 AST、映射依赖关系、检测循环依赖、跟踪异步链、评估类型安全性，并将架构智能提供给 AI 编码助手。
由 EliotShift 构建 · 在 16 个真实项目中经过实战检验 · 100% 通过率。
AI 编码助手编写代码很快，但它们缺乏架构记忆。它们无法记住：
哪些文件耦合紧密
循环依赖存在于何处
哪些类型正在跨边界传播
哪些文件更改过于频繁（热点）
架构层级是什么
LORE 通过 CLI 分析和 MCP 服务器集成，为你的 AI 助手提供对代码库架构的深度理解，从而解决了这些问题。
Related MCP server: Continuum
# | 分析器 | 功能 |
1 | AST 解析器 | 使用 ts-morph + 正则表达式混合实现的完整 TypeScript/TSX 解析 |
2 | 依赖图 | 映射项目中所有的导入、导出和重新导出 |
3 | 循环依赖检测器 | 查找循环并按严重程度进行排名 |
4 | 依赖方向检查器 | 强制执行层级规则（例如，禁止 controller → DB 导入） |
5 | 香农熵 | 每个文件的复杂度评分（简单 → 非常复杂） |
6 | 热点分析 | Git 变动检测 — 更改过于频繁的文件 |
7 | 导入影响分析器 | 显示每个导入的影响范围 |
8 | 类型安全评分器 | 对你的 |
9 | 隐藏耦合检测器 | 通过共享类型查找隐式依赖 |
10 | AI 建议 | 优先修复建议 (P0–P3) |
11 | 工具配置检查器 | 验证 ESLint、Prettier、tsconfig 设置 |
12 | 破坏性变更检测器 | 标记高风险的弃用模式 |
13 | 架构缺口查找器 | 识别缺失的抽象和模式 |
将 LORE 暴露给 Claude Desktop、VS Code、Cursor 或任何 MCP 客户端：
工具 | 描述 |
| 全项目分析 — 评分、违规、复杂度、热点 |
| 健康评分：整体、类型安全、工具、架构 |
| 循环依赖、层级违规、架构缺口 |
| AI 改进建议 (P0–P3) |
| Git 变动频繁的文件（红/黄/绿） |
| 香农熵复杂度报告 |
| 文件导入、导出、消费者、复杂度 |
| 深度 TS 分析：框架、层级、异步链、类型流 |
资源 | 描述 |
| 最新分析结果 (JSON) |
| 深度架构图 (JSON) |
| 环境和缓存状态 (JSON) |
lore [path] Analyze project (default: cwd)
lore analyze [path] Explicit analysis
lore init Extract architectural decisions
lore status View decisions by category
lore diff Diff against saved baseline
lore doctor Environment + tooling check
lore doctor --fix Auto-fix project setup
lore ignore List/manage ignore patterns
lore watch Watch + re-analyze on change
lore mcp inspect Inspect MCP server setup
lore mcp config Claude Desktop config snippet
lore version Show version页面 | 描述 |
完整功能概述、徽章和快速入门 | |
npm、npx 和 Docker 安装 | |
所有 CLI 命令：init、status、doctor、watch、diff 等 | |
Claude Desktop、Cursor 和 Windsurf 设置 | |
Express、NestJS、Next.js 等的真实项目分析 | |
常见问题解答 |
npm install -g lore-mcp# Analyze current project
lore
# Analyze a specific project
lore ./my-typescript-project
# Check environment and auto-fix
lore doctor --fix
# Watch for changes
lore watch --filter src/
# Diff from last baseline
lore diff将其添加到你的 Claude Desktop 配置中：
macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
Linux: ~/.config/Claude/claude_desktop_config.json
Windows: %APPDATA%\Claude\claude_desktop_config.json
{
"mcpServers": {
"lore": {
"command": "npx",
"args": ["-y", "lore-mcp"]
}
}
}或者如果已全局安装：
{
"mcpServers": {
"lore": {
"command": "lore",
"args": ["mcp"]
}
}
}重启 Claude Desktop，然后询问：
"Analyze my project architecture" → LORE 运行
analyze_architecture"What are the circular dependencies?" → LORE 运行get-violations"Which files are hotspots?" → LORE 运行get-hotspots"What are the AI recommendations?" → LORE 运行get-recommendations
lore-mcp/
├── src/
│ ├── algorithm/ # LoreGraph, AST parser, async chain builder, type tracker
│ ├── algorithms/ # AI recommendations, hotspot analysis, entropy, scoring
│ ├── analyzer/ # Dependency parsers, circular deps, direction, imports
│ ├── commands/ # CLI: doctor, diff, ignore, init, status, watch
│ ├── core/ # Plugin system, pipeline runner, graph engine
│ ├── lib/ # Hidden coupling, gaps, middleware chain, ontology
│ ├── mcp/ # MCP server + architecture bridge
│ ├── output/ # Formatter, markdown, SARIF, logger
│ ├── plugins/built-in/ # 13 built-in analysis plugins
│ ├── storage/ # Cache and decision store
│ ├── types/ # TypeScript type definitions
│ ├── cli.ts # CLI entry point
│ └── index.ts # MCP server entry point
├── package.json
└── tsconfig.jsonLORE 使用基于插件的架构 — 每个分析器都是一个插件：
interface LorePlugin {
name: string;
version: string;
analyze(context: AnalysisContext): Promise<PluginResult>;
}内置插件包括：circular-deps, coupling-matrix, dep-direction, entropy, gaps, hidden-coupling, hotspot, import-impact, middleware-chain, breaking-changes, type-safety, tooling-config, ai-recommendations。
解析 — LORE 使用混合 ts-morph + 正则表达式解析器解析所有 .ts/.tsx 文件
构建图 — 构建带有类型化边（导入、类型引用、装饰器、异步链、实现、扩展）的依赖图
运行插件 — 13 个分析器通过插件管道并行运行
评分 — 计算健康评分（类型安全、工具、架构，整体 0–100）
推荐 — AI 引擎生成优先级建议（P0 关键 → P3 建议）
服务 — 结果可通过 CLI 输出、MCP 工具或 SARIF 格式获取
项目 | 文件数 | 结果 | |
Express | 42 | 100% 通过 | |
Next.js | 68 | 100% 通过 | |
Fastify | 55 | 100% 通过 | |
NestJS | 38 | 100% 通过 | |
Prisma | 45 | 100% 通过 | |
Zod | 35 | 100% 通过 | |
TypeORM | 60 | 100% 通过 | + 9 更多 |
所有 16 个测试项目：100% 通过率，零崩溃。
Node.js >= 18.0.0
TypeScript 项目（分析 .ts 和 .tsx 文件）
Git（可选，用于热点分析）
ripgrep（可选，用于更快的发现文件）
组件 | 技术 |
语言 | TypeScript 5.5+ |
AST 解析 | ts-morph 21 |
协议 | 模型上下文协议 (MCP) SDK 1.0 |
传输 | Stdio (兼容 Claude Desktop / IDE) |
验证 | Zod schemas |
输出 | ANSI 终端, Markdown, SARIF |
MIT © 2025 EliotShift
Get all architectural decisions for this project. Call this at the START of every session to understand the codebase.
| Name | Required | Description | Default |
|---|---|---|---|
| category | No | Filter by category (optional) | |
| query | No | Search for specific decisions (optional) |
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 the tool retrieves decisions but doesn't describe key behaviors: whether it returns all decisions at once or paginates, what format the output is in, if there are rate limits, or authentication requirements. The instruction to call at session start implies it's foundational but lacks operational details.
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 highly concise and front-loaded: two sentences with zero waste. The first sentence states the purpose, and the second provides usage guidance, both earning their place without redundancy or fluff.
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 and no output schema, the description is incomplete for a tool that likely returns complex data (architectural decisions). It covers purpose and high-level usage but lacks details on output format, error handling, or behavioral constraints. For a retrieval tool with 2 parameters and no structured output info, this is minimally adequate but has clear gaps.
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 both optional parameters ('category' and 'query') with descriptions. The description doesn't add any meaning beyond what the schema provides—it doesn't explain how filtering or searching works, example categories, or query syntax. Baseline 3 is appropriate when the schema handles parameter documentation.
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 purpose: 'Get all architectural decisions for this project.' It specifies the resource (architectural decisions) and scope (this project). However, it doesn't explicitly differentiate from sibling tools like 'get_gaps' or 'record_decision' beyond implying this is for retrieval rather than recording.
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 explicit usage guidance: 'Call this at the START of every session to understand the codebase.' This gives clear context for when to use it (session initialization). However, it doesn't specify when NOT to use it or mention alternatives like the sibling tools, leaving some gaps in comparative guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Find architectural gaps — decisions that were made but may not be fully implemented.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It mentions 'Find architectural gaps' but does not disclose behavioral traits such as what data sources are used, whether it's a read-only operation, potential side effects, or output format. This leaves significant gaps in understanding how the tool behaves.
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 wastes no space, making it easy to parse quickly.
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 of finding architectural gaps, no annotations, and no output schema, the description is incomplete. It lacks details on how gaps are identified, what the output includes, or any behavioral context, making it insufficient for an agent to fully understand the tool's operation.
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 tool has 0 parameters with 100% schema description coverage, so the schema fully documents the lack of inputs. The description does not add parameter details, but this is acceptable as there are no parameters to explain, maintaining clarity without redundancy.
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 purpose with a specific verb ('Find') and resource ('architectural gaps'), explaining these as 'decisions that were made but may not be fully implemented.' It distinguishes from siblings like 'get_context' and 'record_decision' by focusing on gap identification rather than context retrieval or decision recording.
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 explicit guidance is provided on when to use this tool versus alternatives. The description implies usage for finding architectural gaps but does not specify prerequisites, exclusions, or comparisons to sibling tools, leaving the agent to infer context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Record an architectural decision. Call this whenever you make a technical choice that affects the codebase structure.
| Name | Required | Description | Default |
|---|---|---|---|
| category | Yes | Category: database, authentication, architecture, api, testing, deployment, security, performance, other | |
| decision | Yes | Short description of the decision made | |
| reason | Yes | Why this decision was made | |
| alternatives | No | Alternatives that were considered and rejected | |
| constraints | No | Constraints or rules that should not be violated | |
| author | No | Who made this decision |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It states the action ('record') but doesn't disclose behavioral traits like how the decision is stored, if it's immutable, requires permissions, or has side effects. This is a significant gap for a tool with mutation implications.
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 front-loads the purpose and usage without any wasted words. It's appropriately sized for 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 no annotations and no output schema, the description adequately covers purpose and usage but lacks details on behavior, return values, or error handling. For a mutation tool with 6 parameters, it's minimally viable but has clear gaps in completeness.
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 fully documents all parameters. The description adds no additional meaning beyond implying the tool is for recording decisions, which aligns with the schema but doesn't enhance parameter understanding. Baseline 3 is appropriate as the schema handles 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 clearly states the verb ('record') and resource ('architectural decision'), specifying it's for technical choices affecting codebase structure. It distinguishes from siblings like 'get_context' and 'get_gaps' by focusing on recording rather than retrieving, but doesn't explicitly differentiate beyond that.
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?
It provides clear context on when to use ('whenever you make a technical choice that affects the codebase structure'), which implicitly distinguishes it from sibling tools that likely retrieve information. However, it lacks explicit exclusions or named alternatives for similar recording tasks.
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.
v0.1.5get_contextget_gapsrecord_decisionScored across 3 tools
Each tool has a clearly distinct purpose: get_context retrieves existing decisions, get_gaps identifies implementation issues, and record_decision logs new choices. There is no overlap in functionality, making tool selection straightforward for an agent.
All tools follow a consistent verb_noun pattern (get_context, get_gaps, record_decision) with clear, descriptive names. The naming style is uniform and predictable throughout the set.
Three tools are appropriate for the server's purpose of managing architectural decisions, covering retrieval, gap analysis, and recording. While slightly minimal, each tool serves a distinct and necessary function without redundancy.
The tool set covers the core lifecycle of architectural decisions: retrieving context, identifying gaps, and recording new decisions. A minor gap exists in updating or deleting decisions, but agents can likely work around this for basic workflows.
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