Un servidor MCP (Model Context Protocol) inteligente que proporciona recomendaciones de patrones de diseño mediante búsqueda híbrida (semántica + palabras clave + aumento de grafo). Accede a 710 patrones de diseño en más de 90 categorías a través de una interfaz de lenguaje natural con arquitectura RAG combinada avanzada.
# Clone and setup
git clone https://github.com/apolosan/design_patterns_mcp.git
cd design_patterns_mcp
# Install dependencies and build (using bun)
bun install
bun run db:setup
# Or using npm (if bun is not installed)
npm install --ignore-scripts
npx tsc
node dist/cli/migrate.js
node dist/cli/seed.js
node dist/cli/generate-embeddings.js
node dist/cli/setup-relationships.jsConfigúralo en tu cliente MCP (Claude Desktop, Cursor, etc.) y comienza a descubrir patrones mediante consultas en lenguaje natural.
Related MCP server: Acemcp
Usa Bun como gestor de paquetes canónico para este repositorio (solo bun install). El archivo de bloqueo es bun.lock.
Nunca copies un directorio .git dentro de dist/data (ni lo incluyas dentro de dist/). Esa ruta debe permanecer como un directorio de datos simple para evitar imágenes de varios gigabytes y fugas de metadatos.
Característica | Descripción |
Motor de búsqueda híbrida | RAG combinado que integra recuperación semántica, por palabras clave (BM25) y aumentada por grafo |
710 patrones | Catálogo completo en más de 90 categorías, incluyendo Feature Flag / Feature Toggle para entrega progresiva y experimentación |
Integración MCP | Integración perfecta con Claude, Cursor y otros clientes MCP |
Caché multinivel | Caché L1 en memoria + L3 SQLite con tasa de aciertos superior al 95% |
Sistema de bus de eventos | Comunicación desacoplada entre servicios mediante pub/sub |
Telemetría y salud | Métricas de rendimiento en tiempo real y monitoreo del sistema |
Arquitectura SOLID | Código limpio y mantenible que sigue las mejores prácticas |
Listo para producción | 654 casos de prueba en 77 archivos de prueba con 100% de tasa de éxito (0 fallos, 0 omitidos) |
Categoría | Cantidad | Ejemplos |
Patrones GoF clásicos | 34 | Factory, Builder, Observer, Strategy, Command |
Patrones arquitectónicos | 56 | MVC, Clean Architecture, Hexagonal, DDD, Feature Flag |
Microservicios y nube | 39 | Circuit Breaker, Saga, Service Mesh |
Ingeniería de datos | 54 | Repository, CQRS, Event Sourcing |
IA/ML y MLOps | 46 | RAG, Fine-Tuning, Model Compression |
Patrones de React | 27 | Hooks, Server Components, Performance |
Blockchain y Web3 | 115 | DeFi, NFTs, Smart Contracts, MEV |
Concurrencia y reactivo | 45 | Producer-Consumer, Actor Model |
Seguridad | 21 | OAuth, RBAC, Zero Trust |
Programación funcional | 26 | Monads, Functors, Higher-Order Functions |
src/
├── adapters/ # External service adapters (LLM, embeddings)
├── cli/ # CLI commands (migrate, seed, embeddings, setup-relationships)
├── core/ # DI Container, configuration builder
├── db/ # Database migrations
├── events/ # Event bus system
├── handlers/ # MCP request handlers (hybrid search, recommendations)
├── health/ # Health check services
├── repositories/ # Data access layer
├── search/ # Hybrid search engine
├── services/ # Business services (cache, telemetry, pattern service)
├── strategies/ # Strategy pattern implementations
├── types/ # TypeScript type definitions
└── mcp-server.ts # MCP server entry point
data/
├── patterns/ # 710 JSON pattern definitions (see `feature-flag.json`)
└── design-patterns.db # SQLite database with 710 patterns and embeddingsHaz preguntas en lenguaje natural a través de tu cliente MCP:
"I need to create complex objects with many optional configurations"
→ Builder, Abstract Factory, Factory Method
"How to handle service failures gracefully in distributed systems?"
→ Circuit Breaker, Bulkhead, Retry, Fallback
"What pattern helps with state-dependent behavior in React?"
→ State Machine, Observer, useReducer
"How to implement secure authentication and authorization?"
→ OAuth 2.0, RBAC, JWT, Zero TrustHerramienta | Descripción |
| Búsqueda híbrida de patrones mediante descripciones de problemas |
| Búsqueda por palabras clave o semántica con filtros |
| Información completa del patrón con ejemplos de código |
| Estadísticas sobre los patrones disponibles |
| Salud del sistema y estado de los servicios |
Node.js >= 18.0.0
Bun >= 1.0.0 (recomendado) o npm >= 8.0.0
bun install
bun run build
bun run db:setupEl script prepare en package.json requiere bun. Si no tienes bun instalado, usa --ignore-scripts para omitirlo y compila manualmente:
npm install --ignore-scripts
npx tsc
# Setup database
node dist/cli/migrate.js
node dist/cli/seed.js
node dist/cli/generate-embeddings.js
node dist/cli/setup-relationships.jsAñade a la configuración de tu cliente MCP (Claude Desktop, Cursor, etc.):
{
"mcpServers": {
"design-patterns": {
"command": "node",
"args": ["/absolute/path/to/design-patterns-mcp/dist/mcp-server.js"],
"env": {
"LOG_LEVEL": "info",
"DATABASE_PATH": "/absolute/path/to/design-patterns-mcp/data/design-patterns.db",
"ENABLE_HYBRID_SEARCH": "true",
"ENABLE_GRAPH_AUGMENTATION": "true",
"EMBEDDING_COMPRESSION": "true",
"ENABLE_FUZZY_LOGIC": "true",
"ENABLE_TELEMETRY": "true",
"ENABLE_MULTI_LEVEL_CACHE": "true"
}
}
}
}Importante: Usa rutas absolutas tanto para
argscomo paraDATABASE_PATH. Los clientes MCP como Cursor no admiten de forma fiable el campocwd, por lo que las rutas relativas se resuelven contra el directorio de inicio del usuario en lugar del directorio del proyecto. Consulta la guía de inicio rápido del proyecto para ver ejemplos de configuración específicos de cada cliente.
Variable | Valor por defecto | Descripción |
|
| Nivel de registro (debug, info, warn, error) |
|
| Ruta de la base de datos SQLite |
|
| Habilita la búsqueda RAG combinada |
|
| Habilita el recorrido de relaciones de patrones |
|
| Reducción de dimensionalidad |
|
| Refinamiento de resultados con lógica difusa |
|
| Métricas de rendimiento |
|
| Caché L1 + L3 |
|
| Límite de concurrencia de solicitudes |
|
| Límite de tamaño de caché |
|
| TTL de caché en milisegundos |
|
| Modo de transporte (stdio/http) |
|
| Puerto HTTP (modo http) |
|
| Ruta del endpoint MCP |
|
| Ruta de verificación de salud |
|
| Omitir configuración de la base de datos |
# Build
docker build -t design-patterns-mcp .
# Run HTTP mode
docker run -p 3000:3000 -e TRANSPORT_MODE=http design-patterns-mcp
# Run stdio mode (default)
docker run design-patterns-mcpdocker compose up --build -dVariable | Valor por defecto | Descripción |
|
| Modo de transporte (stdio/http) |
|
| Puerto HTTP (modo http) |
|
| Ruta del endpoint MCP |
|
| Ruta de verificación de salud |
|
| Ruta de la base de datos SQLite |
|
| Nivel de registro |
|
| Omitir configuración de la base de datos |
GET /health - Verificación de salud
POST /mcp - Endpoint JSON-RPC de MCP
# Development
bun run build # Compile TypeScript
bun run dev # Development with hot reload
bun run start # Build and start production server
# Database
bun run db:setup # Complete database setup
bun run migrate # Run migrations
bun run seed # Seed pattern data
bun run generate-embeddings # Generate semantic embeddings
bun run setup-relationships # Setup pattern relationships
# Quality
bun run test # Run all tests
bun run lint # Check code quality
bun run lint:fix # Auto-fix linting issues
bun run typecheck # TypeScript type checkingEl proyecto incluye 654 casos de prueba en 77 archivos de prueba con 100% de tasa de éxito (0 fallos, 0 omitidos):
Pruebas de contrato: Validación de cumplimiento del protocolo MCP
Pruebas de integración: Pruebas de interacción de componentes contra la base de datos SQLite en vivo y los embeddings
Pruebas de rendimiento: Benchmarks de búsqueda y vectorización
Pruebas unitarias: Pruebas de componentes individuales
# Run all tests
bun run test
# Run specific test suites
bun run test:unit -- --grep "PatternService"
bun run test:integration -- --grep "database"
bun run test:performance -- --timeout 30000Este proyecto implementa los patrones que documenta:
Patrón | Implementación |
Repository |
|
Service Layer |
|
Object Pool |
|
Dependency Injection |
|
Strategy |
|
Event Bus |
|
Multi-Level Cache |
|
Builder |
|
El servidor usa sql.js (SQLite WASM) con similitud coseno en memoria sobre los embeddings almacenados. El sqlite-vec nativo (tablas virtuales vec0) no está disponible en este entorno de ejecución. La búsqueda semántica funciona para el tamaño actual del catálogo (~710 patrones) pero no utiliza tablas vectoriales nativas indexadas.
Habilita el enriquecimiento con LLM solo cuando tengas una integración real con un proveedor; los proveedores integrados del puente LLM devuelven marcadores de posición a menos que se amplíen.
¡Las contribuciones son bienvenidas! Consulta la guía de contribución.
Haz un fork del repositorio
Crea una rama de características
Realiza cambios siguiendo los principios SOLID
Ejecuta las pruebas y el linting
Envía una solicitud de extracción
Versión canónica aumentada de 0.7.1 → 0.7.2 en los archivos de código fuente y documentación activos.
Archivos actualizados: package.json, src/mcp-server.ts (registro del servidor + información de tiempo de ejecución), AGENTS.md, CLAUDE.md (encabezado de identidad canónica), README.md (insignia, sección de notas de la versión, pie de página de versión), CHANGELOG.md (nueva entrada). Las referencias de evidencia de auditoría en docs/IMPROVEMENTS_03.md y QUICKSTART.md se conservan sin cambios según el estado #45.
Referencias de versión obsoletas corregidas: encabezado de AGENTS.md y CLAUDE.md (version 0.6.0) → (version 0.7.2) (estaba 7 versiones menores por detrás del package.json real).
El autoinforme de tiempo de ejecución ahora anuncia 0.7.2 a los clientes MCP mediante el constructor Server y el endpoint /info.
Sin cambio de comportamiento a nivel de código: parche puro de canonicalización de versión/lanzamiento.
Pruebas: suite completa conservada (no se modificaron archivos de prueba), typecheck limpio.
Suite de pruebas totalmente validada: 654/654 en verde, 0 omisiones, 0 pendientes (antes 651/654 con 3 fallos intermitentes preexistentes).
Resuelto el problema de data/.git en el pipeline de db:setup, que lo filtraba a data/.git no deseado (prueba de higiene de datos).
Dos pruebas desactualizadas alineadas con el contrato BUG-007: search_type en snake_case se ignora silenciosamente mediante validateSearchPatternsArgs y se aplica la estrategia canónica por defecto hybrid.
tests/unit/input-validation.test.ts
tests/unit/mcp-server-search.test.ts
Corregido el desfase de versión del README.md: el bloque final ahora refleja la línea base canónica 0.7.0 → 0.7.1; la insignia y las secciones Production Ready + Testing están alineadas con la suite actual de 654 pruebas.
Restaurada la línea base de lint: 0 problemas, salida 0 (antes 2 errores + 6 advertencias).
Eliminada la importación no utilizada de HealthStatus en src/mcp/http-transport.ts.
Añadido eslint-disable específico para require-await en el check() síncrono de src/health/embedding-coverage-health-check.ts (se conserva la conformidad de la interfaz).
Refactorizado ensureBM25() en src/handlers/keyword-search-handler.ts para que devuelva BM25Scorer, eliminando 4 aserciones no nulas en los puntos de llamada.
Sustituidas 2 aserciones no nulas sobre results.find() en tests/unit/bm25-scorer.test.ts por comprobaciones explícitas de toBeDefined.
bun run typecheck limpio.
24 nuevos patrones de diseño añadidos desde docs/01_Agentic_Design_Patterns.md y docs/02_Data_Engineering_Design_Patterns.md:
3 de Agentic AI: Multi-Agent Collaboration, Goal Setting and Monitoring, Exception Handling and Recovery.
21 de Data Engineering: Aligned Fan-In, Unaligned Fan-In, Bin Pack Orderer, Exclusive Choice, FIFO Orderer, Fine-Grained Accessor for Resources, Fine-Grained Accessor for Tables, Fine-Grained Tracker, In-Place Overwriter, Isolated Sequencer, Metadata Decorator, Metadata Enhancer, Offline Observer, Schema Compatibility Enforcer, Schema Migrator, Secretless Connector, Secrets Pointer, Single Runner, Skew Detector, SLA Misses Detector, Vertical Partitioner.
El catálogo pasó de 686 → 710 patrones (Agentic AI: 3→6, Data Engineering: 4→25).
Comprobación de integridad superada: 710 patrones, 151 relaciones válidas.
654/654 pruebas superadas (100% en verde, 0 pendientes, 0 esbozos).
Licencia MIT — consulte LICENSE para ver los detalles.
Notas sobre búsqueda vectorial (sql.js vs sqlite-vec) — consulte la guía de contribución
Referencia de la superficie de herramientas MCP
Índice de documentación
Versión: 0.7.2 Última actualización: agosto de 2026 Patrones: 710 definiciones JSON (destacado: Feature Flag / Feature Toggle) Pruebas: 654 casos de prueba | 100% de tasa de éxito (0 fallos, 0 omitidas)
Vea también: el último informe de garantía de calidad adversarial (16 hallazgos: 1 crítico, 9 de prioridad alta, 6 menores). Los hallazgos cubren la corrección del pipeline de búsqueda, el desfase de configuración, la seguridad del transporte y la sincronización de la documentación.
Get the total number of design patterns in the database
| Name | Required | Description | Default |
|---|---|---|---|
| includeDetails | No | Include breakdown by category |
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 states the tool 'Get[s] the total number', implying a read-only operation, but doesn't mention any behavioral traits such as performance considerations, error handling, or whether it's a simple count versus an aggregated query. 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 any unnecessary words. It is appropriately sized and front-loaded, making it easy for an agent to parse quickly. Every part of the sentence earns its place by conveying essential information.
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 tool's low complexity (one optional parameter) and high schema coverage, the description is adequate but has clear gaps. It lacks output schema information, usage guidelines, and behavioral context, which could help an agent use it correctly. However, for a simple counting tool, it meets the minimum viable threshold without being fully comprehensive.
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 input schema has 100% description coverage, with the parameter 'includeDetails' clearly documented in the schema. The description adds no additional meaning beyond what the schema provides, as it doesn't mention parameters at all. According to the rules, when schema_description_coverage is high (>80%), the baseline score is 3, which is appropriate here.
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 ('Get') and resource ('total number of design patterns in the database'), making it easy to understand what it does. However, it doesn't explicitly differentiate from sibling tools like 'find_patterns' or 'search_patterns', which might also retrieve pattern information, so it doesn't reach the highest score.
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 like 'find_patterns', 'get_pattern_details', or 'search_patterns'. It lacks any context about use cases, prerequisites, or exclusions, leaving the agent to infer usage based on the name alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Find design patterns matching a problem description using semantic search
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | Natural language description of the problem or requirements | |
| categories | No | Optional: Pattern categories to search in | |
| maxResults | No | Maximum number of recommendations to return | |
| programmingLanguage | No | Target programming language for implementation examples |
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 'semantic search' but doesn't explain what this entails—e.g., how results are ranked, whether it's a read-only operation, or any limitations like rate limits or authentication needs. For a tool with no annotation coverage, this leaves critical behavioral traits unspecified.
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 function without unnecessary words. It is front-loaded with the core purpose ('Find design patterns...'), making it easy to understand at a glance. Every part of the sentence earns its place by conveying essential information.
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 tool's complexity (semantic search with 4 parameters) and the absence of both annotations and an output schema, the description is insufficient. It doesn't cover behavioral aspects like result format, error handling, or how semantic search works, nor does it explain the relationship between parameters and outcomes. This leaves significant gaps for an AI agent to use the tool 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?
Schema description coverage is 100%, meaning all parameters are documented in the schema itself. The description adds no additional meaning beyond the schema, such as explaining the 'semantic search' process in relation to the 'query' parameter or how 'categories' affect results. Since the schema does the heavy lifting, the baseline score of 3 is appropriate, but no extra value is provided.
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: 'Find design patterns matching a problem description using semantic search.' It specifies the verb ('Find'), resource ('design patterns'), and method ('semantic search'), making the intent unambiguous. However, it doesn't explicitly differentiate from sibling tools like 'search_patterns' or 'count_patterns,' which prevents a perfect score.
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. With siblings like 'search_patterns' and 'count_patterns' available, it fails to indicate scenarios where this tool is preferred, such as for semantic versus keyword-based searches, or how it differs from 'get_pattern_details.' This lack of comparative context leaves the agent without clear usage direction.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get detailed information about a specific pattern
| Name | Required | Description | Default |
|---|---|---|---|
| patternId | Yes | Pattern ID to get details for |
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 states it 'gets' information, implying a read-only operation, but doesn't disclose any behavioral traits such as error handling, permissions needed, rate limits, or what 'detailed information' includes. This is a significant gap for a tool with no annotation coverage.
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 any wasted words. It is appropriately sized and front-loaded, making it easy to understand 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 lack of annotations and output schema, the description is incomplete. It doesn't explain what 'detailed information' entails, potential return values, or any behavioral context. For a tool with no structured data beyond the input schema, this leaves significant gaps in understanding its full functionality.
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 input schema has 100% description coverage, with the parameter 'patternId' clearly documented. The description adds no additional meaning beyond what the schema provides, such as format examples or constraints. With high schema coverage, the 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 verb 'Get' and the resource 'detailed information about a specific pattern', which is specific and understandable. However, it doesn't differentiate from sibling tools like 'find_patterns' or 'search_patterns' that might also retrieve pattern information, so it doesn't reach the highest score.
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 like 'find_patterns' or 'search_patterns'. It lacks context about whether this is for retrieving details of a known pattern ID versus searching for patterns, leaving usage unclear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Search patterns by keyword or semantic similarity
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | Search query | |
| searchType | No | hybrid | |
| limit | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions search functionality but fails to describe critical behaviors such as pagination, rate limits, authentication needs, or what happens on no matches. For a search 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 extremely concise at one sentence with zero waste, front-loading the core functionality. Every word earns its place, making it easy for an agent to parse quickly without unnecessary elaboration.
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 tool's complexity as a search function with 3 parameters, no annotations, and no output schema, the description is incomplete. It lacks information on return values, error handling, and behavioral traits, leaving the agent with insufficient context to use the tool effectively beyond basic 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 description coverage is low at 33%, with only the 'query' parameter documented. The description adds value by explaining that searches can be by keyword or semantic similarity, which helps interpret the 'searchType' enum, but it doesn't clarify the 'limit' parameter or provide details beyond what the schema implies. This partial compensation justifies a baseline score.
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 as searching patterns using keyword or semantic similarity, which is specific and actionable. However, it doesn't explicitly distinguish this from sibling tools like 'find_patterns' or 'count_patterns', leaving some ambiguity about when to choose one over another.
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 like 'find_patterns' or 'count_patterns'. It mentions search methods but doesn't specify scenarios, prerequisites, or exclusions, leaving the agent to guess based on tool names alone.
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.
count_patternsfind_patternsget_pattern_detailssearch_patternsScored across 4 tools
Multiple tools have overlapping purposes that could cause confusion. 'find_patterns' and 'search_patterns' both appear to search for patterns, with descriptions that are nearly identical ('semantic search' vs 'keyword or semantic similarity'), making them difficult to distinguish. 'count_patterns' and 'get_pattern_details' are clearer, but the overlap between the two search tools significantly reduces disambiguation.
All tool names follow a consistent verb_noun pattern with snake_case, such as 'count_patterns', 'find_patterns', 'get_pattern_details', and 'search_patterns'. This uniformity makes the tool set predictable and easy to understand, with no deviations in naming conventions.
With only 4 tools, the set feels thin for a server focused on design patterns, which typically involve operations like creating, updating, or categorizing patterns. While basic retrieval functions are covered, the low count may limit functionality for more complex agent tasks, placing it in the borderline range for appropriateness.
The tool set is severely incomplete for a design patterns domain, as it only supports read operations (counting, searching, and getting details) with no ability to create, update, delete, or manage patterns. This lack of CRUD coverage creates significant gaps that will likely cause agent failures when trying to perform full lifecycle tasks.
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