STRATA
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., "@STRATARemember that the user prefers Python and PostgreSQL for backend projects"
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.
š STRATA ā Universal Context Bridge
STRATA is a high-performance Model Context Protocol (MCP) server that serves as a Universal Context Bridge for AI assistants and LLM agents (Claude Desktop, Cursor, Continue.dev, ChatGPT, etc.).
It gives AI models persistent memory across conversations and coding sessions, backed by Supabase PostgreSQL, full-text indexing, and pgvector semantic search.
ā” Features
š§ Persistent Context Memory: AI agents can save decisions, design systems, API contracts, preferences, and code snippets across sessions.
š Intelligent Memory Search: Fast full-text and contextual search with automatic recency fallbacks.
š Modern MCP 2.0 SDK: Built using the latest
MCPServerdecorator architecture with Server-Sent Events (SSE) streaming transport.šļø Supabase & PostgreSQL: Battle-tested cloud database layer with pgvector support and Row-Level Security (RLS).
š Singleton Connection Pooling: Cached client instances for high-throughput, low-latency tool execution.
𩺠Built-in Health Checks: REST endpoints for uptime monitoring and connectivity status (
/health).
Related MCP server: memory-mcp
š ļø Available MCP Tools
Tool | Description | Parameters |
| Save context, preferences, decisions, or code into long-term memory |
|
| Search past context and discussions matching keywords or queries |
|
| List recent context entries stored in STRATA |
|
| Remove a specific memory entry by UUID |
|
| View database connection status, memory count, and metadata | None |
š Quick Start
1. Prerequisites
Python 3.10 or higher
A Supabase account & project
2. Clone & Setup Environment
# Clone the repository
git clone https://github.com/your-username/STRATA.git
cd STRATA
# Copy environment file and configure your keys
cp .env.example .envEdit .env with your Supabase credentials:
SUPABASE_URL=https://your-project.supabase.co
SUPABASE_KEY=your-supabase-service-role-key
DEFAULT_USER_ID=your-user-uuid (optional)3. Install Dependencies
cd apps/api
python -m venv venv
# Activate virtual environment:
# Windows (PowerShell):
.\venv\Scripts\Activate.ps1
# macOS / Linux:
source venv/bin/activate
# Install requirements
pip install -r requirements.txt4. Database Setup
Run the SQL schema located in supabase_schema.sql in your Supabase SQL Editor to initialize the tables, vector extensions, and full-text indexes.
5. Start the Server
# From apps/api directory:
uvicorn main:app --reload --port 8000The server starts on http://localhost:8000 with the following endpoints:
SSE Endpoint:
http://localhost:8000/sseMessages Endpoint:
http://localhost:8000/messages/Health Check:
http://localhost:8000/health
š Connecting to MCP Clients
Claude Desktop
Add this to your claude_desktop_config.json:
macOS:
~/Library/Application Support/Claude/claude_desktop_config.jsonWindows:
%APPDATA%\Claude\claude_desktop_config.json
{
"mcpServers": {
"strata": {
"url": "http://localhost:8000/sse"
}
}
}Cursor IDE
In Cursor settings under Features > MCP Servers, add a new server:
Name:
STRATAType:
sseURL:
http://localhost:8000/sse
š Project Structure
STRATA/
āāā apps/
ā āāā api/ # Python MCP Server
ā āāā main.py # Server logic, MCPServer instance & tools
ā āāā requirements.txt # Python dependencies
ā āāā venv/ # Python virtual environment
āāā .env.example # Template environment file
āāā .gitignore # Version control ignores
āāā package.json # Root monorepo configuration
āāā supabase_schema.sql # Database schema, pgvector, and indexes
āāā README.md # Documentationš”ļø License
MIT License. See LICENSE for details.
bounce-mcp
This server cannot be deployed
Maintenance
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