Store important facts, decisions, user preferences, and project context for long-term retrieval. Memories persist across sessions with automatic chunking and duplicate detection.
MIT
650,494 tools. Updated 2026-10-13 14:21
"Methods to Store and Learn Dynamic Context Memories" matching MCP tools:
- Save key-value pairs to persistent memory across sessions for remembering user preferences, installed skills, or project context.MIT
- Explain a type's fields and its role in methods (construct, mutate, consume, destroy) from graph facts. Learn a struct's shape and handling before reading call sites.AGPL 3.0
- Retrieve memories whose path patterns match the given file, surfacing context to load before editing.MIT
- Retrieve saved memories at session start to restore context, installed skills, and user preferences. Filter by type for specific categories.MIT
- Store observations as persistent memories with optional tags, importance levels, context, and auto-expiration to manage AI agent memory.MIT
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Official Microsoft Learn MCP Server – real-time, trusted docs & code samples for AI and LLMs.
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- Search and list memories within a space. Use a query to filter semantically or get all memories up to a limit.MIT
- Process raw conversation messages to automatically extract and store useful memories, facts, decisions, preferences, and lessons.MIT
- Retrieve relevant memories for user queries to fetch prior context, preferences, and facts before responding.MIT
- List all stored memories for the current user from both Arc and Atlas in the shared store.MIT
- Save observations, preferences, project context, or decisions to an agent's persistent memory. Reinforces identical memories instead of duplicating them.MIT
- Extract and store important information from conversations to maintain persistent memory across interactions, enabling AI assistants to recall facts and user context.MIT
- Persist agent memories (facts, decisions, context) with an importance score to control decay and retention.MIT
- Retrieve project context at session start: recent memories and an AI summary covering architecture, tech stack, and patterns. Use this to avoid manual memory recall.MIT
- Retrieve data from Aspro.Cloud using list and get methods. Run aspro_describe first to learn parameters, then pass entity id and query arguments to get status, URL, and data.MIT
- Retrieve aggregate counts of stored memories and session events for monitoring. Check store health or confirm data was saved after a session.MIT
- Store raw chat messages as conversation history so a nightly pipeline can later distill them into long-term memories. Use memory_upsert to save a fact immediately.AGPL 3.0
- Persist key information as memories in a local database for cross-session use. Record preferences, lessons, project context, and relationship notes to be recalled later.MIT
- Retrieve paginated memories from an agent's archival store with search, sorting, and filtering capabilities.MIT
- Store key decisions, insights, or notes in a private memory wiki that auto-titles, tags, and links them to related memories for easy future recall.MIT
- Search stored memories using natural language to get relevant context for your current task. Results are ranked by relevance, recency, and importance.MIT