MCPatterns
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@MCPatterns search for my TypeScript error handling patterns"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
MCPatterns
MCPatterns is a Model Context Protocol (MCP) server that enables users to save and retrieve personalized coding patterns. It helps LLMs learn how an individual codes by storing structured patterns categorized by technology, use case, and style.
🧠Purpose
This server acts as a persistent memory layer for LLM agents, allowing them to reference a user's preferred coding styles, patterns, and conventions. It supports:
Personalized code generation based on stored patterns
Consistent refactoring following user preferences
Style-aware suggestions using familiar patterns
Long-term memory of coding practices across sessions
Related MCP server: Claude Habitat
🧩 MCP Integration
MCPatterns follows the Model Context Protocol specification, providing tools for creating, reading, updating, and deleting coding patterns. It uses JSONL (newline-delimited JSON) storage for atomic operations and data consistency.
🗂 Pattern Schema
interface Pattern {
name: string; // Unique identifier
category: string; // e.g., "Backend", "Frontend", "Database"
description: string; // What this pattern does
use_cases: string[]; // When to use this pattern
technologies: string[]; // Languages, frameworks, libraries
code_examples: { [language: string]: string }; // Code samples by language
}Example Pattern
{
"name": "Error Handling Middleware",
"category": "Backend",
"description": "Express middleware for consistent error handling with structured responses",
"use_cases": ["API development", "Middleware composition", "Error standardization"],
"technologies": ["Node.js", "Express", "TypeScript"],
"code_examples": {
"JavaScript": "app.use((err, req, res, next) => {\n console.error(err.stack);\n res.status(500).json({ error: 'Something went wrong!' });\n});",
"TypeScript": "app.use((err: Error, req: Request, res: Response, next: NextFunction) => {\n console.error(err.stack);\n res.status(500).json({ error: 'Something went wrong!' });\n});"
}
}🔧 Available Tools
MCPatterns provides the following MCP tools:
create_patterns
Create multiple new coding patterns in the database.
Input:
{
"patterns": [Pattern, ...]
}add_code_examples
Add new code examples to existing patterns.
Input:
{
"additions": [
{
"patternName": "string",
"examples": { "language": "code" }
}
]
}delete_patterns
Delete multiple patterns by name.
Input:
{
"patternNames": ["pattern1", "pattern2"]
}delete_code_examples
Remove specific code examples from patterns.
Input:
{
"deletions": [
{
"patternName": "string",
"languages": ["JavaScript", "TypeScript"]
}
]
}read_patterns
Retrieve all stored patterns.
Input: None
search_patterns
Search patterns by query across all fields.
Input:
{
"query": "search term"
}open_patterns
Retrieve specific patterns by name.
Input:
{
"names": ["pattern1", "pattern2"]
}🗄 Storage
MCPatterns uses JSONL (newline-delimited JSON) format for data storage:
File location: Configurable via
PATTERNS_FILE_PATHenvironment variableDefault location:
patterns.jsonin the server directoryFormat: Each line contains a JSON object with
type: "pattern"Atomic operations: Full file rewrite ensures data consistency
🚀 Getting Started
Installation
git clone https://github.com/nicholasrubright/mcpatterns.git
cd mcpatterns
pnpm installDevelopment
pnpm run devBuilding
pnpm run buildRunning
pnpm start
# or
mcpatterns🔧 Configuration
Environment Variables
PATTERNS_FILE_PATH: Custom path for the patterns database fileCan be absolute path or relative to script directory
Defaults to
patterns.jsonin server directory
Claude Desktop Integration
Add to your claude_desktop_config.json:
{
"mcpServers": {
"mcpatterns": {
"command": "npx",
"args": ["-y", "@mcpatterns/server"]
}
}
}With Custom Storage Path
{
"mcpServers": {
"mcpatterns": {
"command": "npx",
"args": ["-y", "@mcpatterns/server"],
"env": {
"PATTERNS_FILE_PATH": "/path/to/custom/patterns.json"
}
}
}
}VS Code Integration
Add to your VS Code settings (settings.json):
{
"mcp": {
"servers": {
"mcpatterns": {
"command": "npx",
"args": ["-y", "@mcpatterns/server"]
}
}
}
}💡 Usage Tips
For LLM Agents
MCPatterns works best when integrated into your AI workflow with prompts like:
Before generating code, search my patterns for relevant examples using the technologies I'm working with. Use my established patterns and coding style preferences when creating new code.Pattern Organization
Use descriptive names that clearly identify the pattern's purpose
Group related patterns with consistent category naming
Include comprehensive use cases to improve searchability
Provide examples in multiple languages when applicable
Best Practices
Atomic patterns: Store focused, single-purpose patterns
Rich metadata: Include detailed use cases and technology tags
Version examples: Keep code examples up-to-date with current practices
Search-friendly: Use descriptive language in descriptions and use cases
📜 License
MIT
Available Tools
7 toolsadd_code_examplesC
Add new code examples to existing patterns
Inputs: additions (required).
create_patternsC
Create multiple new coding patterns in the database
Inputs: patterns (required).
delete_code_examplesC
Delete specific code examples from patterns
Inputs: deletions (required).
delete_patternsC
Delete multiple patterns from the database
Inputs: patternNames (required).
open_patternsC
Open specific patterns by their names
Inputs: names (required).
read_patternsB
Read all patterns from the database
Inputs: no named parameters.
search_patternsC
Search for patterns based on a query across all fields
Inputs: query (required).
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
Loading tool schema changelog…
TDQS
Scored across 7 tools
Most tools have distinct purposes, but 'open_patterns' and 'read_patterns' could cause confusion as both involve accessing patterns, with 'open' being less clear about its specific function compared to 'read' for retrieval. The other tools clearly target different operations like creation, deletion, addition, and search.
All tool names follow a consistent verb_noun pattern using snake_case, such as 'add_code_examples', 'create_patterns', and 'search_patterns'. This uniformity makes the tool set predictable and easy to understand at a glance.
With 7 tools, the count is well-scoped for managing coding patterns, covering core operations like create, read, search, delete, and example management. Each tool appears to serve a necessary function without being excessive or insufficient for the domain.
The tool set provides good coverage for CRUD operations on patterns and code examples, including create, read, search, and delete. However, there is a minor gap in update functionality for patterns or examples, which might require workarounds, though the domain is still largely covered.
Related MCP Connectors
Team memory for AI coding agents and code reviews, reviewed by humans and served over MCP.
Persistent memory and cross-session learning for AI coding assistants (hosted remote MCP).
An MCP memory server. One memory your agents share — across models, devices and apps.
Hosted MCP memory for coding agents: persistent across sessions, editable markdown, team sharing.
Related MCP Servers
- AlicenseNot gradedqualityDmaintenanceA local MCP server providing persistent memory for AI coding assistants by storing and searching architectural decisions, patterns, and solutions. It also includes tools for git automation and mapping codebase expertise based on project history.MIT
- FlicenseNot gradedqualityNot gradedmaintenanceAn MCP server that provides persistent project context, workflow management, and knowledge capture for AI coding agents. It enables agents to maintain structured memory across sessions by tracking project profiles, conventions, skills, and technical debt.7-
- AlicenseBqualityBmaintenanceMCP server providing persistent engineering memory and spec-driven development workflows for AI coding agents, preserving learnings across sessions.41Business Source 1.1
- AlicenseNot gradedqualityDmaintenanceMCP server that provides persistent memory and contextual awareness to language models, enabling project onboarding, recall of architectural rules, and code consistency across sessions.32MIT