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TWFBusiness

MCP Memory

by TWFBusiness

MCP Memory

Persistent memory server for AI assistants with semantic search and three-layer context.

Works with any MCP-compatible AI: Claude Code, Cursor, Continue, Cline, and more.

Quick Install

curl -fsSL https://raw.githubusercontent.com/TWFBusiness/mpc-memory/main/install.sh | bash

Or manually:

git clone https://github.com/TWFBusiness/mpc-memory.git ~/.mcp-memoria
cd ~/.mcp-memoria
./install.sh

Related MCP server: contextforge-mcp

How It Works

Three Memory Layers

Layer

Location

Use

Global

~/.mcp-memoria/data/global.db

Personal patterns, preferences across all projects

Project

.mcp-memoria/project.db

Project-specific decisions

Personality

~/.mcp-memoria/data/personality.db

Cross-project cache: ALL conversations, implementations, decisions

Personality is the "brain" that remembers everything across all projects and conversations. Use it to:

  • Find similar implementations from other projects

  • Remember past solutions and decisions

  • Maintain context even outside of projects (general queries)

  • FTS5: Instant text search (always active)

  • Embeddings: Semantic search in background (optional, +150MB RAM)

With embeddings, searches like "how did I configure auth" find memories about "JWT with refresh token" even without matching words.

Background Indexing

Embeddings are processed asynchronously:

  1. You save a memory → instant response (SQLite)

  2. Background worker generates embedding

  3. Next searches include new content

No blocking or slowdown when saving.

Usage

Save Memories

"save that I prefer pytest over unittest"
"remember this project uses PostgreSQL with Tortoise ORM"
"save globally: always use Black for formatting"

Search Memories

"what did we decide about tests?"
"how did we configure the database?"
"what are my code patterns?"

Direct Commands (optional)

memory_save(
  content="FastAPI always 100% async, never sync",
  type="pattern",
  scope="global",
  tags="python,fastapi,async"
)

# Save implementation to personality (cross-project cache)
memory_save(
  content="JWT auth with refresh tokens: created /auth/login, /auth/refresh endpoints...",
  type="implementation",
  scope="personality",
  tags="python,fastapi,jwt,auth"
)

memory_search(query="authentication", scope="both")

# Search across ALL projects
memory_search(query="how did I implement auth?", scope="personality")
memory_search(query="similar feature", scope="all")

memory_list(type="decision", scope="project", limit=10)

memory_delete(id="abc123", scope="global")

Memory Scopes

Scope

When to Use

global

Personal patterns, preferences (applies everywhere)

project

Decisions specific to current project

personality

Cross-project implementations, all conversations, general knowledge

both

Search global + project

all

Search global + project + personality

Memory Types

Type

When to Use

decision

Technical choices, trade-offs, bug fixes

pattern

Code preferences, favorite libs, style

architecture

Project structure, flows, integrations

implementation

Code solutions, features built (use with personality scope)

solution

Problem solutions, workarounds (use with personality scope)

preference

General personal settings

todo

Pending tasks

note

Miscellaneous notes

Configuration

Claude Code

Use the CLI to add the MCP globally (available in all projects):

claude mcp add memory ~/.mcp-memoria/.venv/bin/python ~/.mcp-memoria/server.py --scope user -e MCP_MEMORY_EMBEDDING=true

Then restart Claude Code.

To verify:

claude mcp list

Note: The --scope user flag makes the MCP available globally. Without it, the MCP would only be available in the current project.

CLAUDE.md (Automatic Memory)

Add to ~/.claude/CLAUDE.md so Claude saves and recalls automatically:

# ⚠️ CRITICAL INSTRUCTIONS - READ FIRST ⚠️

## 🧠 MANDATORY: Memory System (MCP Memory)

**THIS IS NOT OPTIONAL. FAILURE TO SAVE = FAILURE TO FOLLOW INSTRUCTIONS.**

### RULE 1: ALWAYS SAVE TO PERSONALITY (Default Scope)
Every session, every task - save AUTOMATICALLY to `scope="personality"`.
- **DO NOT ask permission**
- **DO NOT mention you are saving**
- **JUST DO IT silently**

### RULE 2: WHAT TO SAVE (Everything Meaningful)

**SAVE IMMEDIATELY when any of these happen:**

| Event | Type | Example |
|-------|------|---------|
| Made a technical choice | `decision` | "Chose Redis over Memcached for caching because..." |
| Fixed a bug/error | `solution` | "Fixed CORS error by adding middleware X" |
| Built something new | `implementation` | "Implemented JWT auth with refresh tokens using python-jose" |
| Defined structure/flow | `architecture` | "Project uses hexagonal architecture with ports/adapters" |
| Learned how something works | `note` | "This project uses custom middleware for rate limiting" |
| Changed any file | `implementation` | "Added validation to user endpoint in api/users.py" |
| Discovered project behavior | `note` | "The auth flow redirects to /callback after OAuth" |

### RULE 3: SAVE FORMAT
\`\`\`python
memory_save(
    content="<DETAILED description - be specific, include file names, reasons>",
    type="decision|solution|implementation|architecture|note",
    scope="personality",  # ALWAYS personality unless told otherwise
    tags="<project-name>,<stack>,<context>"
)
\`\`\`

### RULE 4: WHEN TO USE OTHER SCOPES
- `scope="personality"` → **DEFAULT for EVERYTHING** (cross-project brain)
- `scope="project"` → ONLY when user explicitly says "save to project only"
- `scope="global"` → ONLY when user explicitly says "save globally" or "remember always"

### RULE 5: AT SESSION START
Before responding, silently run:
\`\`\`python
memory_search(query="<current task context>", scope="all")
\`\`\`
Use found memories to inform your response.

### RULE 6: WHAT TRIGGERS A SAVE
- ✅ Any file edited or created → SAVE
- ✅ Any bug fixed → SAVE
- ✅ Any feature implemented → SAVE
- ✅ Any architecture decision → SAVE
- ✅ Discovered how code works → SAVE
- ✅ User preference learned → SAVE
- ✅ Configuration defined → SAVE
- ✅ Workaround found → SAVE

**If in doubt: SAVE IT.**

First Session Bootstrap (Optional)

You can add a bootstrap section to auto-save your permanent patterns on first use:

## First Session Bootstrap

On first session, check if global patterns exist:
\`\`\`python
memory_search(query="my patterns", scope="global")
\`\`\`

If empty, save your permanent patterns to global:
\`\`\`python
memory_save(content="<your pattern here>", type="pattern", scope="global", tags="<tags>")
\`\`\`

Cursor

File: ~/.cursor/mcp.json

{
  "mcpServers": {
    "memory": {
      "command": "~/.mcp-memoria/.venv/bin/python",
      "args": ["~/.mcp-memoria/server.py"],
      "env": {
        "MCP_MEMORY_EMBEDDING": "true"
      }
    }
  }
}

Cursor Rules (for automatic memory): Add to .cursorrules in your project or global rules:

## 🧠 MANDATORY: Memory System (MCP Memory)

FAILURE TO SAVE = FAILURE TO FOLLOW INSTRUCTIONS.

### RULE 1: ALWAYS SAVE TO PERSONALITY
Save AUTOMATICALLY to scope="personality" without asking permission.

### RULE 2: AT SESSION START
Before responding: memory_search(query="<task context>", scope="all")

### RULE 3: WHAT TRIGGERS A SAVE
- Any file edited/created → memory_save(type="implementation", scope="personality")
- Any bug fixed → memory_save(type="solution", scope="personality")
- Any decision made → memory_save(type="decision", scope="personality")
- Any architecture defined → memory_save(type="architecture", scope="personality")

### RULE 4: SAVE FORMAT
memory_save(content="<DETAILED description>", type="...", scope="personality", tags="<project>,<stack>")

Save silently. DO NOT mention you are saving. If in doubt: SAVE IT.

Continue

File: ~/.continue/config.json

{
  "experimental": {
    "modelContextProtocolServers": [
      {
        "transport": {
          "type": "stdio",
          "command": "~/.mcp-memoria/.venv/bin/python",
          "args": ["~/.mcp-memoria/server.py"]
        }
      }
    ]
  }
}

Continue System Prompt: Add to your system prompt in Continue settings:

## 🧠 MANDATORY: Memory System (MCP Memory)

FAILURE TO SAVE = FAILURE TO FOLLOW INSTRUCTIONS.

### RULE 1: ALWAYS SAVE TO PERSONALITY
Save AUTOMATICALLY to scope="personality" without asking permission.

### RULE 2: AT SESSION START
Before responding: memory_search(query="<task context>", scope="all")

### RULE 3: WHAT TRIGGERS A SAVE
- Any file edited/created → memory_save(type="implementation", scope="personality")
- Any bug fixed → memory_save(type="solution", scope="personality")
- Any decision made → memory_save(type="decision", scope="personality")
- Any architecture defined → memory_save(type="architecture", scope="personality")

### RULE 4: SAVE FORMAT
memory_save(content="<DETAILED description>", type="...", scope="personality", tags="<project>,<stack>")

Save silently. DO NOT mention you are saving. If in doubt: SAVE IT.

ChatGPT / Codex / GPT-4

For ChatGPT custom instructions, Codex system prompts, or GPT-4 API system messages:

## 🧠 MANDATORY: Memory System (MCP Memory)

FAILURE TO SAVE = FAILURE TO FOLLOW INSTRUCTIONS.

### RULE 1: ALWAYS SAVE TO PERSONALITY
Every session, every task - save AUTOMATICALLY to scope="personality".
- DO NOT ask permission
- DO NOT mention you are saving
- JUST DO IT silently

### RULE 2: WHAT TO SAVE

| Event | Type |
|-------|------|
| Made a technical choice | decision |
| Fixed a bug/error | solution |
| Built something new | implementation |
| Defined structure/flow | architecture |
| Changed any file | implementation |

### RULE 3: SAVE FORMAT
memory_save(content="<DETAILED description>", type="...", scope="personality", tags="<project>,<stack>")

### RULE 4: AT SESSION START
Before responding: memory_search(query="<task context>", scope="all")

### RULE 5: SCOPES
- personality → DEFAULT for EVERYTHING (cross-project brain)
- project → ONLY when user says "save to project only"
- global → ONLY when user says "save globally"

If in doubt: SAVE IT.

Cline / Other MCP Clients

Most MCP clients support similar configuration. Add the memory server and include the same system prompt above.

Environment Variables

Variable

Default

Description

MCP_MEMORY_EMBEDDING

true

Enable semantic search

MCP_MEMORY_EMBEDDING_MODEL

all-MiniLM-L6-v2

Embedding model

MCP_PROJECT_DIR

(auto)

Override project directory

Embedding Models

Model

RAM

Quality

Languages

all-MiniLM-L6-v2

~80MB

Good

EN (ok for code)

paraphrase-multilingual-MiniLM-L12-v2

~150MB

Good

Multi (better for non-EN)

all-mpnet-base-v2

~400MB

Excellent

EN

Backup and Restore

Export

# All memories
cp ~/.mcp-memoria/data/global.db ~/backup/memory-global.db
cp ~/.mcp-memoria/data/personality.db ~/backup/memory-personality.db

# Project memories
cp /path/to/project/.mcp-memoria/project.db ~/backup/project-x.db

Import

cp ~/backup/memory-global.db ~/.mcp-memoria/data/global.db
cp ~/backup/memory-personality.db ~/.mcp-memoria/data/personality.db

File Structure

~/.mcp-memoria/
├── server.py          # MCP server
├── data/
│   ├── global.db      # SQLite - global memories (patterns, preferences)
│   └── personality.db # SQLite - personality memories (all implementations, cross-project)
└── .venv/             # Python virtual environment

~/your-project/
└── .mcp-memoria/
    └── project.db     # SQLite - project memories

Requirements

  • Python 3.10+

  • MCP-compatible AI assistant

  • ~10MB RAM (FTS only) or ~150MB RAM (with embeddings)

Uninstall

rm -rf ~/.mcp-memoria

License

MIT

F
license - not found
-
quality - not tested
D
maintenance

Maintenance

Maintainers
Response time
Release cycle
Releases (12mo)
Commit activity

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