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590,993 tools. Updated 2026-09-20 09:30

"Understanding Cursor or Cline Context in Long-term Memory Systems" matching MCP tools:

  • Enable or disable automatic renewal for long-term rentals and dedicated numbers using a rental ID and a boolean flag.
    MIT
  • Store important facts, decisions, user preferences, and project context for long-term retrieval. Memories persist across sessions with automatic chunking and duplicate detection.
    MIT
  • Store one atomic fact to long-term memory, enabling future sessions to recall it. Automatically deduplicates and surfaces conflicts.
    AGPL 3.0
  • Retrieve stored information from long-term memory using semantic meaning, keywords, or both to provide context about topics when users ask questions.
    MIT
  • Save any statement, event, or instruction to long-term memory. Automatically routes general facts, episodes, and procedures so they persist beyond the current conversation.
    Apache 2.0

Matching MCP Servers

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    license
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    A persistent, self-organizing memory MCP server for AI assistants, using semantic search, knowledge graphs, and reinforcement learning to automatically manage and retrieve memories.
    13
    MIT

Matching MCP Connectors

  • Words-in-context vocabulary practice questions with distractor explanations.

  • A forum whose members are AI agents. Publish verifiable findings, enter scored challenges.

  • Store a new memory in long-term storage for explicitly requested knowledge, decisions, or learnings, with optional links to correct or supersede existing memories.
    -
  • Generate verified MCP configuration files for Claude Desktop, Cursor, Roo Code/Cline, or OpenAI Responses API. Maps local stdio and remote Cloudflare SSE streams for AI agent compatibility.
    MIT
  • Search long-term memory to retrieve anything relevant to a query, including earlier sessions. Results are ranked by relevance and tagged by memory layer.
    Apache 2.0
  • Search shared long-term memory across all agents and sessions to retrieve prior decisions, preferences, project facts, and past incidents before assuming or asking.
    -
  • Reduce memory bloat by merging near-duplicate memories and summarizing related clusters into concise long-term memories. Compress episodic experiences into semantic knowledge.
    MIT
  • Remove stored information from persistent memory by specifying its unique identifier to maintain accurate long-term context.
    MIT
  • Promote a session episode to a persistent engram when it deserves long-term memory. Add tags, scope, and domain to make it retrievable.
    Apache 2.0
  • Recall relevant long-term memories by keyword to restore session context and surface past work, preferences, or project history. Returns entries with type, importance, and similarity.
    MIT
  • Store important context, decisions, and preferences in long-term memory for recall across sessions. Optionally checks for similar existing memories and sensitive data before saving.
    MIT
  • Record a new observation or goal into the session's working memory for context tracking.
    MIT
  • Guards Xcode agent sessions against context compaction and Axint drift by checking project memory, active sessions, and freshness, ensuring proof before long tasks or edits.
    Apache 2.0
  • Save a memory to short-term storage, applying decay over time and promoting frequently used memories to long-term retention.
    MIT
  • Save a memory to short-term storage. Memories decay over time unless reinforced, and frequently used memories may be promoted to long-term.
    MIT