Search stored memories using natural language queries, balancing semantic relevance with memory freshness and importance. Returns both private and team memories when configured.
MIT
127,427 tools. Last updated 2026-05-05 16:46
"Semantic search, RAG, and memory systems" matching MCP tools:
- Retrieve a complete list of all files stored in the RAG system for document management and semantic search operations.MIT
- Search agent memory using semantic similarity to retrieve relevant context across sessions, filter by agent, role, or session, and find stored information through natural language queries.MIT
- Retrieve comprehensive context from all memory systems using semantic search to enhance AI assistant capabilities in retaining short-term, long-term, and episodic memory.MIT
- Find relevant documents in the RAG system using semantic search with customizable similarity thresholds and result limits.MIT
- Manage team semantic memory storage in FleetQ: search, add, delete, and organize knowledge for AI agents.
Matching MCP Servers
- Alicense-qualityBmaintenanceAn advanced MCP server providing RAG-enabled memory through a knowledge graph with vector search capabilities, enabling intelligent information storage, semantic retrieval, and document processing.Last updated1546MIT
- Alicense-qualityCmaintenanceEnables semantic search over markdown files to find related notes by meaning rather than keywords, and automatically detect duplicate content before creating new notes.Last updated1BSD 2-Clause "Simplified"
Matching MCP Connectors
Medical RAG: semantic search for clinical guidelines, drug interactions, diagnoses & EHR data.
AI memory with 56 tools. Knowledge Graph, semantic search, OAuth 2.1 + Magic Link. Free tier.
- Convert text into semantic embeddings for similarity search, clustering, and RAG applications using Saptiva AI's embedding model.
- Initialize a new vector-based RAG project for semantic book search and organization within your Calibre ebook library.
- Add books to a RAG project for vectorization and context search, enabling semantic search and retrieval from your Calibre ebook library.
- Retrieve information from cognitive memory using RAG queries across short-term and long-term memory stores, triggering rehearsal on relevant results.
- Combine multiple working memory entries into a single semantic memory to organize and preserve information for AI systems.
- Combine multiple working memories into a single semantic memory to maintain long-term continuity for AI systems.
- Create migration plans, validate system compatibility, and execute data transfers between different memory systems.MIT
- Execute a complete RAG workflow in a single step to answer user questions: generate embeddings, perform semantic search, and answer using only retrieved context.
- Retrieve memories by text from label, description, why_matters, and tags. Returns only live entries. With Ollama, adds semantic search with distance field. Use recall with ID for full details.MIT
- Search within a Presidential Circular (CB Genelgesi) using keyword or semantic query. Provide the official gazette date from prior search results.MIT
- Search specific Turkish statutes' articles using keyword queries with Boolean operators or semantic AI search for natural language questions.MIT
- Filter documents by metadata before ranking by vector similarity to enable production RAG and semantic search pipelines.MIT
- Search persistent memory using semantic and emotional relevance ranking to retrieve stored facts, decisions, and preferences. Returns results sorted by relevance, recency, and emotional resonance.
- Search within a specific legislation number using keyword Boolean operators or natural language semantic queries to find matching articles.MIT