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.
164,696 tools. Last updated 2026-05-31 14:29
"Information on Rag and Memory" matching MCP tools:
- Get answers to questions by retrieving relevant information from wiki content using RAG technology.
- Get answers to questions by retrieving relevant information from wiki content using RAG technology.
- Execute a full RAG pipeline to answer user questions by retrieving relevant document chunks and quoting them directly.MIT
- Generate vector embeddings from text for semantic search, RAG, clustering, or similarity tasks. Choose between query or document input type and adjust model quality and dimensionality.MIT
- Retrieve detailed memory information including timestamps, metadata, and categories using a specific memory ID from the Memsolus MCP Server.MIT
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 updated4346MIT
- Alicense-qualityFmaintenanceMemory Bank Server provides a set of tools and resources for AI assistants to interact with Memory Banks. Memory Banks are structured repositories of information that help maintain context and track progress across multiple sessions.Last updated10346MIT
Matching MCP Connectors
Medical RAG: semantic search for clinical guidelines, drug interactions, diagnoses & EHR data.
ship-on-friday MCP — wraps StupidAPIs (requires X-API-Key)
- Retrieve all shared memory pools in the workspace, showing name, access level, member count, and memory count for collaborative agent memory spaces.MIT
- Retrieve a list of all knowledge folders in your organization, each containing documents for RAG capabilities.MIT
- Analyze Linux memory dumps using Volatility 2 plugins to extract forensic data like processes, network connections, and system information for security investigations.MIT
- Stores a knowledge fragment with source and evidence tier metadata for future retrieval via semantic RAG queries.MIT
- Loads episodic context for a new task by combining RAG store, vault history, and sealed handoff data. Returns JSON with rag_matches, vault_history, handoff, and continuity type.MIT
- Lists all available RAG categories indexed by RAGMap to help you identify suitable retrieval servers for your task.MIT
- Retrieve detailed system profile information for specific hosts, including CPU, memory, network, disk, BIOS, and software details for RHEL.Apache 2.0
- Retrieve and display all available memory libraries with metadata including library names, node counts, and modification dates for organized information access.MIT
- Search memory to retrieve related insights, hypotheses, and evidence from previous reasoning sessions, helping build on established connections and solutions.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
- Create a searchable knowledge tool for retrieving documents. Integrates selected knowledge folders into a custom tool for RAG-based document search.MIT
- Store facts, notes, or information in shared memory. Automatically classifies, checks conflicts, and builds a knowledge graph.MIT
- Parse code files into semantic chunks such as functions, classes, and methods to improve retrieval in RAG systems.MIT