Search Tenzir documentation by keyword to find operators, functions, or concepts, and explore related content through cross-references for comprehensive understanding.
Apache 2.0
310,808 tools. Last updated 2026-07-28 09:25
"Understanding Memory and Related Concepts" matching MCP tools:
- Discover semantically related concepts using ConceptNet's similarity algorithms to expand exploration, find related terms, and understand semantic neighborhoods.GPL 3.0
- Retrieve comprehensive details about a function or class: signature, parameters, callers, callees, and related domain concepts – without reading its source file. Ideal for understanding what a symbol does and its role in the codebase.MIT
- Save concepts and their definitions into semantic memory for targeted retrieval. Include related concepts and source details to enrich knowledge graphs.AGPL 3.0
- BFS walk from a memory node to map related concepts, supersession chains, or decision clusters. Returns neighbor nodes with typed edges. Read-only; use after searching memories to explore connections.MIT
- Add multiple memory nodes with automatic similarity linking. Computes embeddings and creates connections between related concepts, files, or notes for semantic intelligence.MIT
Matching MCP Servers
- FlicenseAqualityAmaintenanceIntegrates Xiaomi MiMo's multimodal API to enable understanding of images, audio, and video through natural language prompts.Last updated3
- AlicenseCqualityDmaintenanceAn MCP server that analyzes local or remote GitHub repositories, providing intelligent code context and structure to AI coding assistants.Last updated1013MIT
Matching MCP Connectors
One memory, every AI. A shared, user-owned markdown memory your AI clients read and write over MCP.
Persistent long-term memory for AI agents: semantic search, knowledge graph, and task canvas.
- Search memory to retrieve related insights, hypotheses, and evidence from previous reasoning sessions, helping build on established connections and solutions.MIT
- Traverse knowledge graphs from seed nodes to discover related concepts and relationships for depth-first exploration and structured insight generation.Sleepycat
- Traverse related concepts in a SKOS vocabulary using breadth-first search to a specified depth. Find connections starting from a concept URI.MIT
- Search across all memory layers—content, decisions, mistakes, concepts, sessions, and commits—to find relevant information in writing projects.MIT
- Retrieve complete concept details, including broader, narrower, and related concepts, from a SKOS vocabulary.MIT
- Compare and contrast two concepts to uncover key similarities and differences.MIT
- Get autocomplete suggestions for partial queries to discover technical terms, memory addresses, instructions, and concepts from indexed C64 documentation.
- Retrieve entities frequently co-occurring with a given entity, sorted by relationship strength. Useful for discovering related concepts, hardware, and techniques.
- Traverse the knowledge graph from a starting memory to discover related concepts. Specify depth and relationship types to find connections.AGPL 3.0
- File concepts, decisions, and findings with typed relationships to build a persistent, associative memory. Classify each memory by kind and connect related entries to avoid duplication.MIT
- Search Chargebee product documentation for explanations of billing, payments, and subscription management concepts.MIT
- Search Redis documentation and knowledge base to find information on concepts, data structures, features, and use cases including caching, session management, and semantic search.MIT
- Process AntV-related queries by identifying, parsing, and structuring user requirements for visualization tasks. Extracts topics, detects intent, and prepares structured data for precise solutions.MIT
- Search the knowledge graph for entities and their relationships. Discover connections between people, projects, and concepts.MIT