Retrieve stored information from long-term memory using semantic meaning, keywords, or both to provide context about topics when users ask questions.
MIT
136,524 tools. Last updated 2026-05-26 03:09
"Understanding Cursor or Cline Context in Long-term Memory Systems" matching MCP tools:
- Remove stored information from persistent memory by specifying its unique identifier to maintain accurate long-term context.MIT
- Retrieve historical TAIEX index data for analyzing long-term market trends and patterns in Taiwan's stock market.PythonMIT
- Store user preferences, decisions, and project context in long-term memory for recall across future sessions. Preserve technical specifications, coding conventions, and personal choices to maintain consistency.MIT
- Condense a conversation session into structured knowledge and persist it to long-term memory, clearing old tool results to reduce context size.MIT
- Analyze decisions or strategies to detect where short-term gains create long-term fragility. Uses a 6-phase protocol to assess alignment gaps across 9 dimensions at interpersonal, organizational, or systemic scales.
Matching MCP Servers
- Alicense-qualityCmaintenanceProvides persistent long-term memory for AI agents with semantic search and activation-based decay. Enables AI systems to remember across sessions through layered memory architecture and automatic context-aware retrieval.Last updated29MIT
- -license-quality-maintenanceAn MCP server implementing Recursive Language Models (RLM) to process arbitrarily large contexts through a programmatic probe, recurse, and synthesize loop. It enables LLMs to perform multi-step investigations and evidence-backed extraction across massive file sets without being limited by standard context windows.Last updated
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Stop re-explaining yourself to Agents. Give it the right context, right when needed.
- Store information like facts, preferences, or events in MemoVault's long-term memory system for persistence across sessions.MIT
- Store facts, decisions, preferences, or lessons in long-term memory with automatic embedding for semantic retrieval.MIT
- Store user-specific memories like preferences and identity for persistence across sessions. Use for long-term facts.MIT
- Rebuild the selected index to update the in-memory cache after file changes in a long-lived session.MIT
- Store new information in short-term memory with temporal decay, where frequently accessed content gets promoted to long-term storage automatically.AGPL 3.0
- Creates or updates a node in a persistent graph memory for long-term RAG retrieval. Requires initialized NFT matrix; if missing, purchase license key first.Business Source 1.1
- Retrieve the subgraph of connected memories around a center node to understand the context of a specific memory or entity. Specify node ID, traversal depth (1-3), and optional workspace path.Business Source 1.1
- Move high-value or frequently used memories to permanent long-term storage like Obsidian vaults, with options for automatic detection and preview mode.AGPL 3.0
- Merge user working memories into compact long-term summaries to retain essential context and streamline memory storage.MIT
- Simulate a long press at element or coordinates to open context menus or initiate text selection.Apache 2.0
- Archives old working memories and generates dense LLM summaries for long-term retention.MIT
- Manually trigger saving all long-term memories to disk for persistent storage in the Brain-MCP server's memory management system.MIT
- Stores assistant messages in short-term memory with configurable content, importance levels, and optional metadata for enhanced contextual recall.MIT
- Modify stored memory content and connections to maintain accurate, organized long-term information in a graph-based storage system.MIT