{"name":"MemWire","description":"Open source & enterprise-ready AI memory infrastructure layer","url":"https://memwirelabs.ai/","version":"1.0.0","protocolVersion":"0.3","preferredTransport":"HTTP+JSON","supportedInterfaces":[{"url":"https://memwirelabs.ai/","protocolBinding":"HTTP+JSON","protocolVersion":"0.3"}],"provider":{"url":"https://memwirelabs.ai/","organization":"MemWire"},"documentationUrl":"https://memwirelabs.ai/","capabilities":{"streaming":false,"pushNotifications":false},"defaultInputModes":["text/plain"],"defaultOutputModes":["text/plain"],"skills":[{"id":"memwire","name":"Memwire","description":"Use when building AI agents that need persistent, context-aware memory across conversations. Reach for MemWire when you need to store user preferences, facts, and conversation history; recall relevant context for LLM prompts; search memories semantically; or ingest and search documents alongside conversation memory. Use for multi-tenant applications requiring data isolation, or when you need fine-grained control over embedding models, vector databases, and memory graph behavior.","tags":[],"url":"https://memwirelabs.ai/.well-known/agent-skills/memwire/skill.md"}]}