Memory Bank MCP

Memory Bank MCP

An MCP server that enables persistent memory, structured thinking sessions, and project-based knowledge management for Claude. It includes specialized coding tools for package discovery and reinvention prevention by validating code against existing libraries and APIs.

Category
Visit Server

README

๐Ÿง  Memory Bank MCP

A Model Context Protocol (MCP) server for Claude Code that enables persistent memory, structured thinking, team collaboration, and project-based knowledge management โ€” with full export, revision, and analysis capabilities. Now featuring comprehensive coding integration to prevent package reinvention and enforce existing API usage.


๐Ÿš€ Features

๐Ÿง  Core Memory Management

  • Session-Based Thinking: Start with a problem and track related insights
  • Persistent Storage: Store and retrieve memories across sessions
  • Collections: Group related memories with clear purposes
  • Revision & Dependencies: Refine ideas, track changes and links

๐Ÿ’ป Coding Integration (NEW)

  • Package Discovery: Auto-scan installed packages and extract API signatures
  • Reinvention Prevention: Validate code against existing libraries before implementation
  • Code Pattern Storage: Store and retrieve proven code templates and examples
  • Coding Sessions: Specialized session types for development workflows
  • Validation Gates: Catch potential issues and suggest existing solutions

๐Ÿ“ฆ Project & Export System

  • Export to Markdown/JSON: Full or filtered memory exports
  • Project Structure Generation: Standardized folders for teams
  • Project Indexing: Maintain status updates and documentation
  • Context Loading: Load ongoing work for seamless continuation

๐Ÿ“Š Search & Analytics

  • Tag-Based Search: Find insights by topics or keywords
  • Importance Scores: Prioritize content using confidence metrics
  • Session Analysis: Detect contradictions, gaps, and themes

โšก Quick Start

git clone https://github.com/spideynolove/memory-bank-mcp
cd memory-bank-mcp
uv sync
uv run main.py

Requires Python 3.10+ and uv


๐Ÿ›  Installation Options

  • Direct run: uv run main.py
  • Global install: uv tool install .
  • Development mode: uv pip install -e .

๐Ÿงช Test

uv run -c "import main; print('Installation successful')"

๐Ÿ”ง Session Workflow (API Example)

Basic Memory Session

create_memory_session(
    problem="Implement user auth",
    success_criteria="Secure + scalable",
    constraints="Use existing DB"
)

store_memory(
    content="Use JWT with refresh",
    tags="auth,jwt",
    importance=0.9
)

analyze_memories()
export_session_to_file("auth_session.md")

Coding Session with Validation

# Start a coding-specific session
create_memory_session(
    problem="Build HTTP client for API integration",
    success_criteria="Efficient, maintainable, using existing libraries",
    constraints="Must handle auth, retries, rate limiting",
    session_type="coding_session"
)

# Discover what packages are already available
discover_packages()

# Check if functionality already exists before coding
prevent_reinvention_check("HTTP client for REST APIs")
# Returns: Found existing APIs: requests.get(), urllib3.request(), etc.

# Validate code before implementation
validate_package_usage("""
def make_request(url):
    import urllib.request
    return urllib.request.urlopen(url).read()
""")
# Returns: Warning - Consider using requests library

# Store proven patterns for reuse
store_codebase_pattern(
    pattern_type="api_usage",
    code_snippet="import requests\nresponse = requests.get(url, headers=headers)",
    description="Standard HTTP GET with auth headers",
    language="python"
)

โš ๏ธ Must start with create_memory_session() before storing anything.


๐Ÿงฉ Session Tools

Core Memory Tools

Tool Description
create_memory_session() Start a new thinking session (now supports session_type)
store_memory() Save insights with tags and confidence (now supports code_snippet)
revise_memory() Update previous memories
create_collection() Group insights
merge_collection() Combine collections
analyze_memories() Run quality checks
export_session_to_file() Export full sessions
export_memories_to_file() Export filtered memories
load_project_context() Resume prior sessions
update_project_index() Document team progress

๐Ÿ’ป Coding Integration Tools (NEW)

Tool Description
discover_packages() Auto-scan installed packages and extract APIs
validate_package_usage() Validate code against existing packages
explore_existing_apis() Find existing APIs for needed functionality
prevent_reinvention_check() Comprehensive reinvention prevention warning
store_codebase_pattern() Store code patterns with metadata
load_codebase_context() Load project structure into memory

Enhanced Tools

  • create_memory_session(): Now accepts session_type parameter for coding_session, debugging_session, architecture_session
  • store_memory(): Now accepts code_snippet, language, pattern_type parameters for code integration

๐Ÿ’ป Coding Session Types & Workflows

Session Types

  • coding_session: General development work with package discovery and validation
  • debugging_session: Problem-solving focused with enhanced error pattern storage
  • architecture_session: System design with emphasis on integration patterns

Validation Workflow

# 1. Start coding session
create_memory_session("Build user service", "Efficient API", session_type="coding_session")

# 2. Discover available packages
discover_packages()
# Scans environment and stores: requests, fastapi, pydantic, etc.

# 3. Check for existing solutions before coding
prevent_reinvention_check("HTTP server framework")
# โš ๏ธ POTENTIAL REINVENTION DETECTED โš ๏ธ
# Found existing APIs: fastapi.FastAPI(), flask.Flask(), etc.

# 4. Validate specific code patterns
validate_package_usage("""
class CustomHTTPServer:
    def __init__(self, port):
        self.port = port
    def start(self):
        # custom server implementation
        pass
""")
# Warning: Consider using FastAPI or Flask instead

# 5. Store proven patterns for team reuse
store_codebase_pattern(
    pattern_type="api_endpoint",
    code_snippet="""
@app.get("/users/{user_id}")
async def get_user(user_id: int):
    return {"user_id": user_id}
""",
    description="Standard FastAPI endpoint pattern",
    language="python",
    tags="api,fastapi,endpoint"
)

๐Ÿ” MCP Resources for Coding

Access coding data through these resources:

Resource Description
codebase://packages View discovered packages in session
codebase://patterns View stored code patterns
codebase://validation-checks View validation check history

Example Usage

# View discovered packages
# Resource: codebase://packages
{
  "requests": [
    {"signature": "requests.get(url, **kwargs)", "usage_example": "requests.get('https://api.example.com')"},
    {"signature": "requests.post(url, data=None, json=None, **kwargs)", "usage_example": "requests.post(url, json={'key': 'value'})"}
  ],
  "fastapi": [
    {"signature": "fastapi.FastAPI()", "usage_example": "app = FastAPI()"}
  ]
}

# View code patterns  
# Resource: codebase://patterns
[
  {
    "id": "abc123",
    "type": "api_endpoint", 
    "description": "Standard FastAPI endpoint",
    "language": "python",
    "tags": ["api", "fastapi"],
    "code_snippet": "@app.get('/users/{user_id}')\nasync def get_user(user_id: int)..."
  }
]

๐Ÿ— Project Structure

memory-bank/
โ”œโ”€โ”€ thinking_sessions/
โ”œโ”€โ”€ domain_knowledge/
โ”œโ”€โ”€ implementation_log/
โ”œโ”€โ”€ exports/
โ”œโ”€โ”€ project_knowledge_index.md
โ””โ”€โ”€ README.md

Initialize with:

create_project_structure("My Project")

๐Ÿง‘โ€๐Ÿคโ€๐Ÿง‘ Collaboration Patterns

Developer A - Research Phase

create_memory_session("Research auth libs", "Evaluate options", session_type="coding_session")
discover_packages()  # Find available auth libraries
prevent_reinvention_check("JWT authentication")
store_memory("Use FastAPI JWT plugin", code_snippet="from fastapi_jwt import JWT", language="python")
export_session_to_file("thinking_sessions/research_alice.md")

Developer B - Implementation Phase

load_project_context("memory-bank")
create_memory_session("Design auth flow", "Complete plan", session_type="architecture_session")

# Load existing patterns from team
# Resource: codebase://patterns shows Alice's JWT pattern

validate_package_usage("""
def custom_jwt_encode(payload):
    import base64
    return base64.b64encode(json.dumps(payload).encode())
""")
# Warning: Consider using existing JWT libraries

export_memories_to_file("domain_knowledge/auth_decisions.json")

Developer C - Debugging Phase

create_memory_session("Fix auth token expiry", "Resolve production issue", session_type="debugging_session")
explore_existing_apis("JWT token refresh")
store_codebase_pattern("debugging", "Token expiry logs", "Check exp claim in JWT payload")

๐Ÿ’ฅ Error Recovery

try:
    analyze_memories()
except:
    create_memory_session("Recovery", "Rebuild context")

Safe export:

def safe_export(path):
    try:
        export_session_to_file(path)
    except:
        export_session_to_file(path.replace(".md", ".json"))

๐Ÿง‘โ€๐Ÿ’ผ Role-Based Usage

Role Actions
Lead create_project_structure(), update_project_index(), discover_packages()
Developer create_memory_session(), validate_package_usage(), store_codebase_pattern()
New Teammate load_project_context(), explore_existing_apis(), prevent_reinvention_check()

๐Ÿ—„๏ธ Database Schema Extensions

The coding integration adds 4 new tables to the SQLite database:

New Tables

-- Package APIs discovered in sessions
CREATE TABLE package_apis (
    id TEXT PRIMARY KEY,
    session_id TEXT NOT NULL,
    package_name TEXT NOT NULL,        -- e.g., "requests"
    api_signature TEXT NOT NULL,       -- e.g., "requests.get(url, **kwargs)"
    usage_example TEXT,                -- e.g., "requests.get('https://api.com')"
    documentation TEXT,                -- API documentation excerpt
    discovered_at TIMESTAMP,
    usage_count INTEGER DEFAULT 0
);

-- Code patterns stored for reuse
CREATE TABLE codebase_patterns (
    id TEXT PRIMARY KEY,
    session_id TEXT NOT NULL,
    pattern_type TEXT NOT NULL,        -- 'api_usage', 'integration', 'structure'
    code_snippet TEXT NOT NULL,       -- Actual code
    description TEXT,                  -- Human description
    language TEXT,                     -- 'python', 'javascript', etc.
    file_path TEXT,                    -- Original file path if applicable
    tags_json TEXT DEFAULT '[]',       -- JSON array of tags
    created_at TIMESTAMP,
    updated_at TIMESTAMP
);

-- Coding session metadata
CREATE TABLE coding_sessions (
    session_id TEXT PRIMARY KEY,
    session_type TEXT NOT NULL,       -- 'coding_session', 'debugging_session', 'architecture_session'
    project_path TEXT,               -- Project directory
    language TEXT,                   -- Primary language
    framework TEXT,                  -- Primary framework
    packages_discovered INTEGER,     -- Count of packages found
    patterns_stored INTEGER,         -- Count of patterns stored
    validation_checks INTEGER        -- Count of validations run
);

-- Validation check results  
CREATE TABLE validation_checks (
    id TEXT PRIMARY KEY,
    session_id TEXT NOT NULL,
    check_type TEXT NOT NULL,         -- 'package_usage', 'reinvention_prevention'
    target_code TEXT NOT NULL,       -- Code that was validated
    result TEXT NOT NULL,            -- 'passed', 'failed', 'warning'
    message TEXT,                    -- Human-readable result
    suggestions_json TEXT,           -- JSON array of suggestions
    created_at TIMESTAMP
);

Migration & Compatibility

  • Automatic Migration: New tables created automatically when first used
  • Backwards Compatible: Existing sessions continue to work unchanged
  • Schema Evolution: Database adapts seamlessly to new features
  • Data Isolation: Coding features are project-specific via session isolation

๐Ÿ“˜ Best Practices

General Memory Management

  • Always start with create_memory_session()
  • Use specific tags for search/export
  • Run analyze_memories() before final export
  • Use collections for structure
  • Track everything under version control

Coding Integration Best Practices

  • Start coding sessions with package discovery: discover_packages() first
  • Check for reinvention before coding: Use prevent_reinvention_check() early
  • Validate code patterns: Run validate_package_usage() before implementation
  • Store proven patterns: Use store_codebase_pattern() for team knowledge sharing
  • Load project context: Always load_codebase_context() when joining existing projects
  • Use appropriate session types:
    • coding_session for general development
    • debugging_session for problem-solving
    • architecture_session for system design

Team Workflow

# 1. Project Lead: Set up discovery
create_memory_session("Project kickoff", "Team alignment", session_type="architecture_session")
discover_packages()  # Establish baseline
load_codebase_context()  # Scan existing code

# 2. Developers: Check before coding
prevent_reinvention_check("user authentication")  # Before starting work
validate_package_usage(proposed_code)  # Before committing

# 3. Knowledge Sharing: Store patterns
store_codebase_pattern("error_handling", error_code, "Team standard error pattern")
export_session_to_file("team_standards.md")

๐Ÿงฉ Claude Desktop Integration

{
  "mcpServers": {
    "memory-bank": {
      "command": "uv",
      "args": ["run", "/path/to/memory-bank-mcp/main.py"]
    }
  }
}

๐Ÿ“„ License

MIT License


๐Ÿ†˜ Support

  • Open an issue on GitHub
  • Read the usage examples above

Recommended Servers

playwright-mcp

playwright-mcp

A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.

Official
Featured
TypeScript
Audiense Insights MCP Server

Audiense Insights MCP Server

Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.

Official
Featured
Local
TypeScript
Magic Component Platform (MCP)

Magic Component Platform (MCP)

An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.

Official
Featured
Local
TypeScript
VeyraX MCP

VeyraX MCP

Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.

Official
Featured
Local
Kagi MCP Server

Kagi MCP Server

An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.

Official
Featured
Python
graphlit-mcp-server

graphlit-mcp-server

The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.

Official
Featured
TypeScript
Qdrant Server

Qdrant Server

This repository is an example of how to create a MCP server for Qdrant, a vector search engine.

Official
Featured
Neon Database

Neon Database

MCP server for interacting with Neon Management API and databases

Official
Featured
Exa Search

Exa Search

A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.

Official
Featured
E2B

E2B

Using MCP to run code via e2b.

Official
Featured