MCP Synaptic

by jvanmelckebeke

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GitHub Website

Knowledge Base

About

Memory-enhanced MCP server with local RAG database and expiring memory capabilities

Explore

- Expiring memories with configurable TTL and automatic cleanup
- Support for ephemeral, short-term, long-term, and permanent memory types
- Optional Redis backend for distributed memory storage
- ChromaDB-based vector database with semantic search
- API-based or local embedding models (sentence-transformers)
- Full MCP protocol, SSE, and WebSocket real-time communication
- Docker-ready with multi-service orchestration

Setting up with Highlight

This MCP is not yet compatible with Highlight’s one-click setup. However, you can still use it with Highlight by following these steps:

Download and install Highlight from highlightai.com/download
Navigate to the plugins tab and select "Add Custom Plugin"
Configure the plugin with the settings below

Plugin Name MCP Synaptic

Command (node, npx, python, etc.)

Please refer to the README for specific instructions on how to obtain API keys or other required environment variables.
Enable "Start Automatically" if you want the plugin to start when Highlight launches

Setting up with Cursor

Open Cursor's settings
Navigate to the MCP section
Click "Add new Global MCP server"
Copy and paste the configuration below based on your operating system

JSON

{
    "mcpServers": {
        "mcp synaptic": {
            "mcp-synaptic": {
                "command": "uv",
                "args": [
                    "sync"
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "mcp synaptic": {
            "mcp-synaptic": {
                "command": "uv",
                "args": [
                    "sync"
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "mcp synaptic": {
            "mcp-synaptic": {
                "command": "uv",
                "args": [
                    "sync"
                ]
            }
        }
    }
}

Setting up with Claude Desktop

Open Claude Desktop's settings
Go to the Integrations tab
Click "Add MCP Server"
Use the configuration below based on your operating system

Note: Make sure Claude Desktop is running before attempting to connect.

JSON

{
    "mcpServers": {
        "mcp synaptic": {
            "mcp-synaptic": {
                "command": "uv",
                "args": [
                    "sync"
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "mcp synaptic": {
            "mcp-synaptic": {
                "command": "uv",
                "args": [
                    "sync"
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "mcp synaptic": {
            "mcp-synaptic": {
                "command": "uv",
                "args": [
                    "sync"
                ]
            }
        }
    }
}

Setting up with Windsurf

Open Windsurf's preferences
Go to the Integrations section
Click "Add MCP Integration"
Use the configuration below based on your operating system

JSON

{
    "mcpServers": {
        "mcp synaptic": {
            "mcp-synaptic": {
                "command": "uv",
                "args": [
                    "sync"
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "mcp synaptic": {
            "mcp-synaptic": {
                "command": "uv",
                "args": [
                    "sync"
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "mcp synaptic": {
            "mcp-synaptic": {
                "command": "uv",
                "args": [
                    "sync"
                ]
            }
        }
    }
}

Setting up with Cline

Open Cline's settings
Navigate to the Integrations section
Click "Add MCP Server"
Copy and paste the configuration below based on your operating system

Note: Make sure to restart Cline after adding the configuration.

JSON

{
    "mcpServers": {
        "mcp synaptic": {
            "mcp-synaptic": {
                "command": "uv",
                "args": [
                    "sync"
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "mcp synaptic": {
            "mcp-synaptic": {
                "command": "uv",
                "args": [
                    "sync"
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "mcp synaptic": {
            "mcp-synaptic": {
                "command": "uv",
                "args": [
                    "sync"
                ]
            }
        }
    }
}

Setting up with Goose

Open Goose's preferences
Go to the Integrations tab
Select "MCP Servers"
Click "Add New Server"
Use the configuration below based on your operating system

JSON

{
    "mcpServers": {
        "mcp synaptic": {
            "mcp-synaptic": {
                "command": "uv",
                "args": [
                    "sync"
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "mcp synaptic": {
            "mcp-synaptic": {
                "command": "uv",
                "args": [
                    "sync"
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "mcp synaptic": {
            "mcp-synaptic": {
                "command": "uv",
                "args": [
                    "sync"
                ]
            }
        }
    }
}

From the repository

- Python 3.11 or higher
- UV package manager
- Docker (optional, for containerized deployment)

1. Build and run with Docker Compose:

   docker-compose up --build

2. Or run individual container:

   docker build -t mcp-synaptic .

   docker run -p 8000:8000 mcp-synaptic

Create a .env file in the project root (use .env.example as template):

SERVER_HOST=localhost SERVER_PORT=8000 DEBUG=false LOG_LEVEL=INFO SQLITE_DATABASE_PATH=./data/synaptic.db CHROMADB_PERSIST_DIRECTORY=./data/chroma DEFAULT_MEMORY_TTL_SECONDS=3600 MAX_MEMORY_ENTRIES=10000 MEMORY_CLEANUP_INTERVAL_SECONDS=300 EMBEDDING_MODEL=text-embedding-3-small EMBEDDING_PROVIDER=api EMBEDDING_API_BASE=http://localhost:4000 EMBEDDING_API_KEY=your-api-key-here MAX_RAG_RESULTS=10 RAG_SIMILARITY_THRESHOLD=0.7 API-based Embeddings (Recommended) - Lightweight deployment without PyTorch dependencies - Works with LiteLLM, OpenAI API, or any OpenAI-compatible endpoint - Set EMBEDDING_PROVIDER=api and configure EMBEDDING_API_BASE Local Embeddings - Includes full PyTorch and sentence-transformers - No external API dependency but much larger container - Set EMBEDDING_PROVIDER=local and install with --extra local-embeddings

bash

uv run mcp-synaptic server --host 0.0.0.0 --port 9000 --debug

uv sync --group dev pre-commit install

bash

Claude Desktop / Cursor

Paste into your MCP client config file to install this server.

{
    "mcpServers": {
        "mcp synaptic": {
            "mcp-synaptic": {
                "command": "uv",
                "args": [
                    "sync"
                ]
            }
        }
    }
}

McpServers

{
    "mcp-synaptic": {
        "command": "uv",
        "args": [
            "sync"
        ]
    }
}

A memory-enhanced MCP (Model Context Protocol) server with local RAG (Retrieval-Augmented Generation) database and expiring memory capabilities.

Features

🧠 Memory Management

- Expiring Memories: Store temporary memories with configurable TTL (Time To Live) - Memory Types: Support for different memory categories (short-term, long-term, ephemeral) - Automatic Cleanup: Background processes to remove expired memories - Redis Integration: Optional Redis backend for distributed memory storage

📚 RAG Database

- Local Vector Storage: ChromaDB-based vector database for document storage - Embedding Models: Built-in support for sentence-transformers models - Semantic Search: Similarity-based document retrieval - Document Management: Add, update, and delete documents with versioning

🔄 Real-time Communication

- Server-Sent Events (SSE): Real-time updates for memory and RAG operations - MCP Protocol: Full Model Context Protocol implementation - WebSocket Support: Alternative real-time communication channel - Event Streaming: Live updates for memory expiration and document changes

🐳 Docker Ready

- Containerized Deployment: Ready-to-use Docker containers - Docker Compose: Multi-service orchestration with Redis and database - Environment Configuration: Flexible configuration through environment variables

Quick Start

Prerequisites

- Python 3.11 or higher
- UV package manager
- Docker (optional, for containerized deployment)

Installation

1. Clone the repository:

   git clone https://github.com/your-org/mcp-synaptic.git

   cd mcp-synaptic

2. Install dependencies:

   # For API-based embeddings (recommended - lightweight)

   uv sync

   

   # For local embeddings (includes PyTorch - heavy)

   uv sync --extra local-embeddings

3. Initialize the project:

   uv run mcp-synaptic init

4. Start the server:

   uv run mcp-synaptic server

The server will start on http://localhost:8000 by default.

Docker Deployment

1. Build and run with Docker Compose:

   docker-compose up --build

2. Or run individual container:

   docker build -t mcp-synaptic .

   docker run -p 8000:8000 mcp-synaptic

Configuration

Environment Variables

Create a .env file in the project root (use .env.example as template):

```env

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About

Explore

Setting up with Highlight

From the repository

Claude Desktop / Cursor

McpServers

Features

🧠 Memory Management

📚 RAG Database

🔄 Real-time Communication

🐳 Docker Ready

Quick Start

Prerequisites

Installation

Docker Deployment

Configuration

Environment Variables

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