SnackMachine
Modular snack registry, scheduler, spool, and MCP knowledge conduit that enables AI-ranked search, multi-agent shared memory, and FTS5 indexing across vault layers.
README
SnackMachine
Modular snack registry, scheduler, spool, and MCP knowledge conduit.
Install
pip install snackmachine
Or from source:
pip install -e .
Structure
snackmachine/
├── registry.py # SnackSpec, SnackPlugin, SnackRegistry
├── base.py # SnackParam, SnackMeta, BaseSnack
├── scheduler.py # Cron-like maintenance scheduler
├── spool_reader.py # Spool feed parser
├── spool_writer.py # Spool append writer
└── conduit/
├── bridge.py # MCP query/status/chat routes
├── library_index.py # FTS5 across all 5 vault layers
├── ai_bridge.py # AI-ranked search with agent boosting
├── doclang.py # Markdown/wiki-link/section parser
└── knowledge_layer.py # Multi-agent shared memory
Usage
from snackmachine.registry import get_registry
from snackmachine.base import BaseSnack, SnackMeta
class MySnack(BaseSnack):
meta = SnackMeta(id="my-snack", name="My Snack", category="custom")
def execute(self, action=None, **kwargs):
return {"success": True}
# Register
get_registry().register(MySnack())
Data
All mutable data lives in ~/.ucore/:
~/.ucore/indices/library.db— FTS5 search index~/.ucore/knowledge/shared.db— multi-agent memory~/.ucore/logs/— spool files~/.ucore/config/mcp-manifests/— MCP server manifests
Set SNACKMACHINE_DATA_DIR to override the data root.
License
MIT
Recommended Servers
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.
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.
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.
VeyraX MCP
Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.
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.
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.
E2B
Using MCP to run code via e2b.
Neon Database
MCP server for interacting with Neon Management API and databases
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.
Qdrant Server
This repository is an example of how to create a MCP server for Qdrant, a vector search engine.