LORE es un motor de arqueología de código basado en plugins para proyectos TypeScript. Analiza tu AST, mapea dependencias, detecta dependencias circulares, rastrea cadenas asíncronas, puntúa la seguridad de tipos y proporciona inteligencia arquitectónica a los asistentes de programación con IA a través del Protocolo de Contexto de Modelo (MCP).
Creado por EliotShift · Probado en batalla en 16 proyectos del mundo real · 100% de tasa de éxito.
Los asistentes de programación con IA escriben rápido, pero carecen de memoria arquitectónica. No pueden recordar:
Qué archivos están estrechamente acoplados
Dónde residen las dependencias circulares
Qué tipos se están propagando a través de los límites
Qué archivos cambian con demasiada frecuencia (puntos críticos o hotspots)
Cuáles son las capas arquitectónicas
LORE resuelve esto dándole a tu asistente de IA una comprensión profunda de la arquitectura de tu base de código, a través del análisis por CLI y la integración con el servidor MCP.
Related MCP server: Continuum
# | Analizador | Qué hace |
1 | Analizador AST | Análisis completo de TypeScript/TSX con ts-morph + híbrido de regex |
2 | Grafo de dependencias | Mapea todas las importaciones, exportaciones y re-exportaciones en tu proyecto |
3 | Detector de dependencias circulares | Encuentra ciclos y los clasifica por gravedad |
4 | Comprobador de dirección de dependencias | Aplica reglas de capa (ej. no permitir importaciones de controlador → BD) |
5 | Entropía de Shannon | Puntuación de complejidad por archivo (simple → muy complejo) |
6 | Análisis de puntos críticos (Hotspots) | Detección de cambios en Git: archivos que cambian con demasiada frecuencia |
7 | Analizador de impacto de importación | Muestra el radio de explosión de cada importación |
8 | Puntuador de seguridad de tipos | Califica tu uso de |
9 | Detector de acoplamiento oculto | Encuentra dependencias implícitas a través de tipos compartidos |
10 | Recomendaciones de IA | Sugerencias de corrección priorizadas (P0–P3) |
11 | Comprobador de configuración de herramientas | Valida la configuración de ESLint, Prettier y tsconfig |
12 | Detector de cambios disruptivos | Marca patrones de obsolescencia de alto riesgo |
13 | Buscador de brechas arquitectónicas | Identifica abstracciones y patrones faltantes |
Expón LORE a Claude Desktop, VS Code, Cursor o cualquier cliente MCP:
Herramienta | Descripción |
| Análisis completo del proyecto: puntuaciones, violaciones, complejidad, puntos críticos |
| Puntuaciones de salud: general, seguridad de tipos, herramientas, arquitectura |
| Dependencias circulares, violaciones de capa, brechas arquitectónicas |
| Sugerencias de mejora de IA (P0–P3) |
| Archivos con alta frecuencia de cambios en git (rojo/amarillo/verde) |
| Informe de complejidad de entropía de Shannon |
| Importaciones, exportaciones, consumidores y complejidad de un archivo |
| Análisis profundo de TS: framework, capas, cadenas asíncronas, flujo de tipos |
Recurso | Descripción |
| Resultados del último análisis (JSON) |
| Grafo de arquitectura profunda (JSON) |
| Estado del entorno y caché (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 versionSitio web: eliotshift.github.io/lore-mcp
Página | Descripción |
Resumen completo de características, insignias e inicio rápido | |
Instalación mediante npm, npx y Docker | |
Todos los comandos CLI: init, status, doctor, watch, diff, etc. | |
Configuración para Claude Desktop, Cursor y Windsurf | |
Análisis del mundo real de Express, NestJS, Next.js y más | |
Respuestas a preguntas comunes |
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 diffAñade esto a tu configuración de Claude Desktop:
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"]
}
}
}O si se instaló globalmente:
{
"mcpServers": {
"lore": {
"command": "lore",
"args": ["mcp"]
}
}
}Reinicia Claude Desktop y luego pregunta:
"Analyze my project architecture" → LORE ejecuta
analyze_architecture"What are the circular dependencies?" → LORE ejecutaget-violations"Which files are hotspots?" → LORE ejecutaget-hotspots"What are the AI recommendations?" → LORE ejecutaget-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 utiliza una arquitectura basada en plugins: cada analizador es un plugin:
interface LorePlugin {
name: string;
version: string;
analyze(context: AnalysisContext): Promise<PluginResult>;
}Los plugins integrados incluyen: circular-deps, coupling-matrix, dep-direction, entropy, gaps, hidden-coupling, hotspot, import-impact, middleware-chain, breaking-changes, type-safety, tooling-config, ai-recommendations.
Analizar (Parse) — LORE analiza todos los archivos .ts/.tsx usando un analizador híbrido de ts-morph + regex
Construir grafo — Construye un grafo de dependencias con bordes tipados (importación, referencia de tipo, decorador, cadena asíncrona, implementa, extiende)
Ejecutar plugins — 13 analizadores se ejecutan en paralelo a través de la tubería de plugins
Puntuar — Calcula puntuaciones de salud (seguridad de tipos, herramientas, arquitectura, general 0–100)
Recomendar — El motor de IA genera sugerencias priorizadas (P0 crítico → P3 deseable)
Servir — Resultados disponibles a través de la salida CLI, herramientas MCP o formato SARIF
Proyecto | Archivos | Resultado | |
Express | 42 | 100% Aprobado | |
Next.js | 68 | 100% Aprobado | |
Fastify | 55 | 100% Aprobado | |
NestJS | 38 | 100% Aprobado | |
Prisma | 45 | 100% Aprobado | |
Zod | 35 | 100% Aprobado | |
TypeORM | 60 | 100% Aprobado | + 9 más |
Los 16 proyectos de prueba: 100% tasa de éxito, cero fallos.
Node.js >= 18.0.0
Proyecto TypeScript (analiza archivos .ts y .tsx)
Git (opcional, para análisis de puntos críticos)
ripgrep (opcional, para un descubrimiento de archivos más rápido)
Componente | Tecnología |
Lenguaje | TypeScript 5.5+ |
Análisis AST | ts-morph 21 |
Protocolo | Model Context Protocol (MCP) SDK 1.0 |
Transporte | Stdio (compatible con Claude Desktop / IDE) |
Validación | Esquemas Zod |
Salida | Terminal ANSI, 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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