Retrieve detailed information about a specific RAG project within the Calibre ebook library, including its configuration, contents, and organization for semantic search and contextual conversations.
349,819 tools. Last updated 2026-07-31 11:18
"Information on Rag and Memory" matching MCP tools:
- Save important information to long-term memory with tags, collections, and workspace support. Append to existing memories to prevent duplicates.AGPL 3.0
- Mark a memory as superseded by a newer one to update or correct information, keeping the old version for audit purposes.MIT
- Store information to client-side encrypted memory. Use to persist facts, decisions, or context across sessions by creating or updating memory cells.Apache 2.0
- Retrieve a list of all knowledge folders in your organization, each containing documents for RAG capabilities.MIT
- Validate and delete a knowledge chunk from a store. Set dryRun to false to permanently remove and stop RAG search.MIT
Matching MCP Servers
- Alicense-qualityBmaintenanceA thin MCP client that provides a search_kb tool for querying a remote oG-Memory knowledge base, enabling any agent to retrieve relevant information without local setup.Last updatedMIT
- Alicense-qualityCmaintenanceAn 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 updated6146MIT
Matching MCP Connectors
Persistent semantic memory for AI agents: store and recall text by meaning (RAG). x402
Medical RAG: semantic search for clinical guidelines, drug interactions, diagnoses & EHR data.
- Retrieve comprehensive system information including kernel version, architecture, hostname, uptime, and memory statistics to monitor system status.GPL 3.0
- Retrieve server health information including version, uptime, memory usage, cache statistics, and Node.js version.MIT
- Get precise answers to any question about Lamatic.ai documentation by searching all indexed docs with RAG.MIT
- Retrieve the full content, metadata, and source of a specific knowledge chunk to inspect what content is used in RAG searches.MIT
- Retrieve aggregated knowledge search and RAG query counts for a project or entire organization within a date range.MIT
- Lists all available RAG categories indexed by RAGMap to help you identify suitable retrieval servers for your task.MIT
- Remove stored information from persistent memory by specifying its unique identifier to maintain accurate long-term context.MIT
- Retrieve detailed Redis server information and statistics. Specify a section such as memory or cpu to filter results.MIT
- Retrieve detailed information and evidence for a specific memory entry by providing the repository and memory ID.MIT
- List documents and web pages ingested into a knowledge store for RAG retrieval, with pagination and filtering.MIT
- Parse code files into semantic chunks such as functions, classes, and methods to improve retrieval in RAG systems.MIT
- Retrieve SSH connection status and comprehensive system details including hardware, memory, and disk usage.GPL 3.0
- Store facts, notes, or information in shared memory. Automatically classifies, checks conflicts, and builds a knowledge graph.MIT
- Search and filter RAG-capable MCP servers by query, categories, score, transport, and other criteria to find the right retrieval server for your task.MIT