Memory: Persistent Context Across Reasoning Cycles

Memory in MCP serves as the persistent, structured storage that an agent can use to reference previous interactions, maintain state, or build context over time. Unlike traditional LLM memory, which is simulated by stuffing previous outputs into the next prompt, MCP memory is external, addressable, and queryable.

Memory can include:

• Prior tool results(e.g., previous API calls)
• Stored user preferences or metadata
• Session state(e.g., what documents have been reviewed)
• Summaries or embeddings of earlier interactions

Each memory object can be assigned an identifier and passed to the model as part of the context. The model can then reason over the memory selectively, rather than relying on prompt-engineered recall. For example, in a multi-turn conversation, a legal assistant agent might retain summaries of past contracts and refer to them when analyzing an new one.

This enables continuity and relevance across interactions. It also allows for sophisticated operations like context-aware retrieval, topic-aware reasoning, and dynamic memory management. Developers can programmatically print, update, or enrich memory between task cycles, supporting long-term agents that improve with use.

Excerpt From: Mastering A2A + MCP for Agentic AI Development