MCP Synaptic
@jvanmelckebeke
About MCP Synaptic
Memory-enhanced MCP server with local RAG database and expiring memory capabilities
Basic information
Config
Add this server to your MCP-compatible client using the configuration below.
{
"mcpServers": {
"mcp-synaptic": {
"command": "uv",
"args": [
"sync"
]
}
}
}Tools
No tools detected
We auto-extract tools from the README. The maintainer can list them under a ## Tools heading to populate this section.
Overview
What is MCP Synaptic?
MCP Synaptic is a memory-enhanced MCP (Model Context Protocol) server that provides a local RAG (Retrieval-Augmented Generation) database and expiring memory capabilities. It is designed for developers who need to store temporary memories with configurable TTL, manage documents via vector search, and stream real-time eventsβall within the MCP ecosystem.
How to use MCP Synaptic?
Install Python 3.11+ and the UV package manager. Run uv sync to install dependencies, then uv run mcp-synaptic init to initialize the project and uv run mcp-synaptic server to start the server on http://localhost:8000. For containerized deployment, use docker-compose up --build. Configuration is managed through environment variables (see .env.example).
Key features of MCP Synaptic
- 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
Use cases of MCP Synaptic
- Store user preferences or session data that automatically expire after a set time
- Build AI assistants that can retrieve relevant documents via semantic search
- Develop multi-agent systems that share short-term context through memory
- Deploy memory-backed MCP servers in containerized environments
FAQ from MCP Synaptic
What embedding options does MCP Synaptic support?
MCP Synaptic supports API-based embeddings (lightweight, works with OpenAI-compatible endpoints) and local embeddings using PyTorch and sentence-transformers (heavier but no external dependency).
What is the default TTL for memories and how can I change it?
The default memory TTL is 3600 seconds (1 hour). You can change it via the DEFAULT_MEMORY_TTL_SECONDS environment variable.
Can I use MCP Synaptic without Docker?
Yes. You can run MCP Synaptic directly with Python 3.11+ and UV. Docker is optional and only needed for containerized deployments.
What happens when a memory expires?
Expired memories are automatically removed by a background cleanup process that runs at a configurable interval (default 300 seconds). Events are emitted via SSE when a memory expires.
Does MCP Synaptic require Redis?
No. Redis is optional and only needed if you want a distributed memory store. By default, MCP Synaptic uses SQLite and local ChromaDB storage.
More Memory & Knowledge MCP servers
Memory Bank MCP Server
alioshrA Model Context Protocol (MCP) server implementation for remote memory bank management, inspired by Cline Memory Bank.
Notion MCP Server
suekouA Model Context Protocol server for connecting Notion to MCP-compatible clients
Mcp Knowledge Graph
shanehollomanMCP server enabling persistent memory for Claude through a local knowledge graph - fork focused on local development
Semantic Scholar MCP Server
YUZongminA FastMCP server implementation for the Semantic Scholar API, providing comprehensive access to academic paper data, author information, and citation networks.
Context7 MCP - Up-to-date Docs For Any Cursor Prompt
upstashContext7 Platform -- Up-to-date code documentation for LLMs and AI code editors
Comments