Retrieve memories associated with specific concepts from a neural memory system. Filter results by activation strength and exclude decayed memories to focus on relevant information.
MIT
114,467 tools. Last updated 2026-04-21 13:35
- Traverse memory graphs to discover related memories by exploring connections from a starting point, with configurable depth and relationship strength parameters.
- Discover semantically related concepts using ConceptNet's similarity algorithms to expand exploration, find related terms, and understand semantic neighborhoods.
- Look up semantic concepts in codebases to find variants, related terms, naming conventions, function signatures, and file locations in one query.MIT
- Visualize codebase domain concepts and directory structure to understand semantic layout before exploring specific elements.MIT
- Browse and filter stored memories with pagination for auditing, exploring, or debugging memory contents in the Recall system.
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- View all tags used across memories with counts to identify existing threads, groups, and find related content by tag.MIT
- Traverse knowledge graphs from seed nodes to discover related concepts and relationships for depth-first exploration and structured insight generation.
- Locate stored memories connected by content similarity, shared tags, or direct links to build structured knowledge relationships.
- Search within related memories using a specific memory as context root to find solutions in relevant knowledge without external leakage.MIT
- Search within related memories to find solutions in specific problem contexts using semantic scoping without embeddings.MIT
- Search Tenzir documentation by keyword to find operators, functions, or concepts, and explore related content through cross-references for comprehensive understanding.Apache 2.0
- Consolidate related AI assistant memories into fewer comprehensive facts using AI synthesis. Groups memories by subject, archives originals with version history, and reduces clutter after multiple sessions.MIT
- Find related memories in Claude sessions using knowledge graph connections and semantic similarity analysis to maintain context continuity.MIT
- Find related Grokipedia articles linked from a specific page to explore connected topics, build knowledge graphs, or conduct follow-up research.MIT
- Add multiple memory nodes with automatic similarity linking. Computes embeddings and creates connections between related concepts, files, or notes for semantic intelligence.MIT
- Retrieve stored memories by ID to access specific solutions, facts, or decisions from a persistent knowledge base, including related memories when needed.MIT
- Identify groups of related memories by similarity to organize and analyze connections within stored data.MIT
- Connect related memories with named relationships to build knowledge graphs, such as linking bug fixes to architecture decisions or connecting preferences to resulting patterns.MIT
- Export all memories as JSON to back up data or transfer between projects.MIT
- Explore concepts, code examples, handouts, or reference projects after submitting answers to enhance learning and understanding.MIT
- Retrieve or search stored memories within a specific context space using semantic filtering to access relevant information.MIT
- Connect related memories in a knowledge graph to establish relationships between concepts. Define connections using relation types like depends_on, implements, or references.MIT
- Documents project details by adding or updating sections in summary.md for purpose, architecture, concepts, patterns, or notes.MIT
- Retrieve stored data by key from persistent memory, optionally including related memories to access structured information and knowledge graphs.
- Export memories from the DocuMCP server to JSON or CSV format for analysis or backup purposes.MIT
- Discover connected memories by traversing relationship graphs from a starting memory, with configurable depth and strength thresholds.
- Browse stored memories in chronological order to review recent activity, audit memory contents, or filter by category for targeted results.MIT
- Organize memories by creating section headers for grouping related content without embedding or visualization.MIT
- Retrieve complete session context including progress, memories, and notes to resume previous work exactly where you left off.MIT
- Look up exact Pine Script API terms and known concepts to get accurate documentation paths for functions like ta.rsi or strategy.entry.MIT
- Discover available ENCODE genomic data by exploring filter counts before searching. Shows how many experiments or files exist for each filter value to help identify datasets.AGPL 3.0
- Retrieve stored memories from the Mem0 MCP Server using structured filters and pagination to browse specific user or agent data without search queries.Apache 2.0
- Search stored memories using precise filters for tags, types, and importance to retrieve specific technical information, acronyms, or known terms.MIT
- Retrieve relevant project memories as formatted context for complex tasks, enabling AI agents to access historical information and constraints when starting multi-step work.MIT
- Retrieve stored memories using structured filters for specific users, agents, or time periods, with pagination support for browsing results.Apache 2.0
- Modify or delete existing memories in MemOS using natural language feedback when no specific IDs are provided.Apache 2.0
- Soft-delete memories by keypath to remove outdated information while preserving full version history. Use this tool to clean up agent memory subtrees or mark memories as no longer relevant.Apache 2.0
- Retrieve paginated memories from an agent's archival store with search, sorting, and filtering capabilities.MIT
- Retrieve structured academic details for courses, assignments, exams, and concepts. Track deadlines, study resources, and progress to plan effectively and stay organized.
- Delete specific memories from the Cortex MCP server by ID, with options for soft deletion (mark as stale) or permanent removal. Use force parameter for protected memories.MIT
- Retrieve stored memories in chronological order for browsing or auditing purposes, with pagination support for efficient navigation.MIT
- Analyze file structure to identify classes, functions, and methods with domain concepts and semantic roles without reading file contents.MIT
- Get function or class details without reading source code — returns signature, parameters, callers, callees, and domain concepts to understand what a symbol does and how it fits into the codebase.MIT
- Compare domain ontology changes between git revisions to identify added, removed, or modified concepts and detect naming inconsistencies across commits.MIT
- Create and manage persistent tasks in your backlog to track work, ideas, and issues that survive across sessions. Update status, link related items, and maintain non-decaying memory for planned activities.Apache 2.0
- Create or update memory nodes in a semantic graph to represent concepts, files, symbols, or notes with auto-generated embeddings for linking and retrieval.MIT
- Match project descriptions to knowledge graph concepts using embedding similarity to identify relevant architectural patterns and generate consultation sessions.AGPL 3.0
- Search stored memories using natural language queries and filters to find relevant information across users, agents, or time periods.Apache 2.0
- Search stored memories using semantic queries to retrieve relevant past context for tasks, project details, or session references.MIT
- List memories in compact format with ID, preview, and tags to reduce context usage and browse efficiently without loading full content.MIT