T1nker 1220_memories With Lessons MCP Server

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What is T1nker-1220_memories-with-lessons-mcp-server

T1nker-1220_memories-with-lessons-mcp-server is an innovative tool developed by MCP-Mirror, designed to enhance the capabilities of AI systems like Claude. It implements a persistent memory system using a local knowledge graph, allowing the AI to remember user-specific information across different interactions. This server not only stores and recalls user data but also learns from past errors through a sophisticated lesson system, improving its responses over time.

How to Use T1nker-1220_memories-with-lessons-mcp-server

Using the T1nker-1220 server involves setting it up with your AI client, such as Claude. The process starts with integrating the server using commands that configure it to store and manage data. Users can perform various operations such as creating entities, defining relationships, and managing lessons based on errors and solutions.

For setup, you can configure the server to run with Docker or NPX, depending on your environment preferences. Once integrated, the server works seamlessly with AI to store observations and lessons in JSON format, providing a rich context for future interactions. Users can execute commands to add new data, update existing information, or retrieve specific nodes and relationships from the knowledge graph.

Key Features of T1nker-1220_memories-with-lessons-mcp-server

Persistent Memory: Utilizes a local knowledge graph to store detailed information about users, including their preferences, behaviors, and interactions. This allows the AI to provide personalized responses.
Entities and Relations: Defines entities (like people, organizations) and their relationships, facilitating a structured data representation. Each entity stores observations, which are individual facts about the entity.
Lesson System: Captures knowledge from past errors, including error patterns and solutions, to improve future performance. This system tracks the success of solutions and updates learning models accordingly.
Comprehensive API Tools: Offers a variety of tools for managing data, including creation, deletion, and retrieval of entities, observations, and relations. Users can also search nodes based on specific queries.
Error Handling and Recommendations: Automates the process of identifying similar errors and recommending solutions based on past data. This feature enhances troubleshooting and problem resolution.
File Management: Efficiently manages data storage in JSON files, splitting them as needed to maintain performance.
Compatibility: Easily integrates with AI clients and supports configuration via environment variables, providing flexibility in setup and operation.

Overall, T1nker-1220_memories-with-lessons-mcp-server is a powerful tool for enhancing AI capabilities, allowing systems to learn and adapt from interactions while maintaining a secure and structured memory of user data.

A basic implementation of persistent memory using a local knowledge graph. This lets Claude remember information about the user across chats and learn from past errors through a lesson system.

Core Concepts

Entities

Entities are the primary nodes in the knowledge graph. Each entity has:

A unique name (identifier)
An entity type (e.g., "person", "organization", "event")
A list of observations Example:

 "name": "John_Smith",
 "entityType": "person",
 "observations": ["Speaks fluent Spanish"]

Relations

Relations define directed connections between entities. They are always stored in active voice and describe how entities interact or relate to each other. Example:

 "from": "John_Smith",
 "to": "Anthropic",
 "relationType": "works_at"

Observations

Observations are discrete pieces of information about an entity. They are:

Stored as strings
Attached to specific entities
Can be added or removed independently
Should be atomic (one fact per observation) Example:

 "entityName": "John_Smith",
 "observations": [
 "Speaks fluent Spanish",
 "Graduated in 2019",
 "Prefers morning meetings"

Lessons

Lessons are special entities that capture knowledge about errors and their solutions. Each lesson has:

A unique name (identifier)
Error pattern information (type, message, context)
Solution steps and verification
Success rate tracking
Environmental context
Metadata (severity, timestamps, frequency) Example:

 "name": "NPM_VERSION_MISMATCH_01",
 "entityType": "lesson",
 "observations": [
 "Error occurs when using incompatible package versions",
 "Affects Windows environments specifically",
 "Resolution requires version pinning"
 "errorPattern": {
 "type": "dependency",
 "message": "Cannot find package @shadcn/ui",
 "context": "package installation"
 "metadata": {
 "severity": "high",
 "environment": {
 "os": "windows",
 "nodeVersion": "18.x"
 "createdAt": "2025-02-13T13:21:58.523Z",
 "updatedAt": "2025-02-13T13:22:21.336Z",
 "frequency": 1,
 "successRate": 1.0
 "verificationSteps": [
 "command": "pnpm add shadcn@latest",
 "expectedOutput": "Successfully installed shadcn",
 "successIndicators": ["added shadcn"]

API

Tools

create_entities
Create multiple new entities in the knowledge graph
Input: entities (array of objects)
Each object contains:
name (string): Entity identifier
entityType (string): Type classification
observations (string): Associated observations
Ignores entities with existing names
create_relations
Create multiple new relations between entities
Input: relations (array of objects)
Each object contains:
from (string): Source entity name
to (string): Target entity name
relationType (string): Relationship type in active voice
Skips duplicate relations
add_observations
Add new observations to existing entities
Input: observations (array of objects)
Each object contains:
entityName (string): Target entity
contents (string): New observations to add
Returns added observations per entity
Fails if entity doesn't exist
delete_entities
Remove entities and their relations
Input: entityNames (string)
Cascading deletion of associated relations
Silent operation if entity doesn't exist
delete_observations
Remove specific observations from entities
Input: deletions (array of objects)
Each object contains:
entityName (string): Target entity
observations (string): Observations to remove
Silent operation if observation doesn't exist
delete_relations
Remove specific relations from the graph
Input: relations (array of objects)
Each object contains:
from (string): Source entity name
to (string): Target entity name
relationType (string): Relationship type
Silent operation if relation doesn't exist
read_graph
Read the entire knowledge graph
No input required
Returns complete graph structure with all entities and relations
search_nodes
Search for nodes based on query
Input: query (string)
Searches across:
Entity names
Entity types
Observation content
Returns matching entities and their relations
open_nodes
Retrieve specific nodes by name
Input: names (string)
Returns:
Requested entities
Relations between requested entities
Silently skips non-existent nodes

Lesson Management Tools

create_lesson
Create a new lesson from an error and its solution
Input: lesson (object)
Contains:
name (string): Unique identifier
entityType (string): Must be "lesson"
observations (string): Notes about the error and solution
errorPattern (object): Error details
type (string): Category of error
message (string): Error message
context (string): Where error occurred
stackTrace (string, optional): Stack trace
metadata (object): Additional information
severity ("low" | "medium" | "high" | "critical")
environment (object): System details
frequency (number): Times encountered
successRate (number): Solution success rate
verificationSteps (array): Solution verification
Each step contains:
command (string): Action to take
expectedOutput (string): Expected result
successIndicators (string): Success markers
Automatically initializes metadata timestamps
Validates all required fields
find_similar_errors
Find similar errors and their solutions
Input: errorPattern (object)
Contains:
type (string): Error category
message (string): Error message
context (string): Error context
Returns matching lessons sorted by success rate
Uses fuzzy matching for error messages
update_lesson_success
Update success tracking for a lesson
Input:
lessonName (string): Lesson to update
success (boolean): Whether solution worked
Updates:
Success rate (weighted average)
Frequency counter
Last update timestamp
get_lesson_recommendations
Get relevant lessons for current context
Input: context (string)
Searches across:
Error type
Error message
Error context
Lesson observations
Returns lessons sorted by:
Context relevance
Success rate
Includes full solution details

File Management

The server now handles two types of files:

memory.json: Stores basic entities and relations
lesson.json: Stores lesson entities with error patterns Files are automatically split if they exceed 1000 lines to maintain performance.

Cursor MCP Client Setup

To integrate this memory server with Cursor MCP client, follow these steps:

Clone the Repository:

git clone [repository-url]
cd [repository-name]

Install Dependencies:

pnpm install

Build the Project:

pnpm build

Configure the Server:

Locate the full path to the built server file: /path/to/the/dist/index.js
Start the server using Node.js: node /path/to/the/dist/index.js

Activate in Cursor:

Use the keyboard shortcut Ctrl+Shift+P
Type "reload window" and select it
Wait a few seconds for the MCP server to activate
Select the stdio type when prompted The memory server should now be integrated with your Cursor MCP client and ready to use.

Usage with Claude Desktop

Setup

Add this to your claude_desktop_config.json:

Docker

 "mcpServers": {
 "memory": {
 "command": "docker",
 "args": ["run", "-i", "-v", "claude-memory:/app/dist", "--rm", "mcp/memory"]

NPX

 "mcpServers": {
 "memory": {
 "command": "npx",
 "args": [
 "-y",
 "@modelcontextprotocol/server-memory"

NPX with custom setting

The server can be configured using the following environment variables:

 "mcpServers": {
 "memory": {
 "command": "npx",
 "args": [
 "-y",
 "@modelcontextprotocol/server-memory"
 "env": {
 "MEMORY_FILE_PATH": "/path/to/custom/memory.json"

MEMORY_FILE_PATH: Path to the memory storage JSON file (default: memory.json in the server directory)

System Prompt

The prompt for utilizing memory depends on the use case. Changing the prompt will help the model determine the frequency and types of memories created. Here is an example prompt for chat personalization. You could use this prompt in the "Custom Instructions" field of a Claude.ai Project.

Follow these steps for each interaction:
1. User Identification:
 - You should assume that you are interacting with default_user
 - If you have not identified default_user, proactively try to do so.
2. Memory Retrieval:
 - Always begin your chat by saying only "Remembering..." and retrieve all relevant information from your knowledge graph
 - Always refer to your knowledge graph as your "memory"
3. Memory
 - While conversing with the user, be attentive to any new information that falls into these categories:
 a) Basic Identity (age, gender, location, job title, education level, etc.)
 b) Behaviors (interests, habits, etc.)
 c) Preferences (communication style, preferred language, etc.)
 d) Goals (goals, targets, aspirations, etc.)
 e) Relationships (personal and professional relationships up to 3 degrees of separation)
4. Memory Update:
 - If any new information was gathered during the interaction, update your memory as follows:
 a) Create entities for recurring organizations, people, and significant events
 b) Connect them to the current entities using relations
 b) Store facts about them as observations

Building

Docker:

docker build -t mcp/memory -f src/memory/Dockerfile .

License

This MCP server is licensed under the MIT License. This means you are free to use, modify, and distribute the software, subject to the terms and conditions of the MIT License. For more details, please see the LICENSE file in the project repository.

New Tools

create_lesson
Create a new lesson from an error and its solution
Input: lesson (object)
Contains error pattern, solution steps, and metadata
Automatically tracks creation time and updates
Verifies solution steps are complete
find_similar_errors
Find similar errors and their solutions
Input: errorPattern (object)
Contains error type, message, and context
Returns matching lessons sorted by success rate
Includes related solutions and verification steps
update_lesson_success
Update success tracking for a lesson
Input:
lessonName (string): Lesson to update
success (boolean): Whether solution worked
Updates success rate and frequency metrics
get_lesson_recommendations
Get relevant lessons for current context
Input: context (string)
Returns lessons sorted by relevance and success rate
Includes full solution details and verification steps

BIG CREDITS TO THE OWNER OF THIS REPO FOR THE BASE CODE I ENHANCED IT WITH LESSONS AND FILE MANAGEMENT

Big thanks! jerome3o-anthropic

How to Use

To use the T1nker-1220_memories-with-lessons-mcp-server, follow these steps:

Visit https://github.com/MCP-Mirror/T1nker-1220_memories-with-lessons-mcp-serverhttps://github.com/MCP-Mi...
Follow the setup instructions to create an account (if required)
Connect the MCP server to your Claude Desktop application
Start using T1nker-1220_memories-with-lessons-mcp-server capabilities within your Claude conversations

Additional Information

Created

February 16, 2025

Company

MCP-Mirror

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