"Malt" matching MCP servers:

Shared research cache for AI agents. Caches web research across sessions and users - hit means instant answer from verified sources, miss means your research saves the next dev's tokens. Semantic search with freshness tracking, gap detection, and real-time token measurement via JSONL. Free, open source.

A Context-as-a-Service MCP server that maintains structured, graph-based context for Shopify apps by extracting and summarizing data from web sources and help centers. It enables multi-agent systems to retrieve isolated, provenance-backed context slices or starter bundles via push and pull mechanisms.

Provides access to over 500 pre-configured YAML templates and guided workflows for fine-tuning, training, and evaluating LLMs like Llama and DeepSeek. It enables AI assistants to search for recipes, retrieve configurations, and validate parameters for various machine learning tasks.

Liminal turns objectives into structured work by strategizing through an atomic, deterministic basis: setting guardrails around scope, compiling resources and constraints, uncovering gaps, and organizing research and next steps. All while carrying continuity and priorities forward so your AI builds on what’s already done.