"Managing Proxmox Servers" matching MCP servers:

Enables read-only searching and retrieval of the official Proxmox VE administration guide via MCP tools and resources, returning cited fragments with source URLs and freshness metadata.

MCP server for MeiliSearch that enables searching indexes, managing documents and indexes, checking health/stats, and managing API keys. Perfect for AI agents to interact with MeiliSearch through natural language.

An MCP server that packs repositories into AI-friendly files using the Repomix tool, supporting local and remote repositories with size estimation and compression. It enables users to efficiently feed entire codebases into AI models while managing context window limits.

Enables agents to run semantic search across one or more local project directories by automatically maintaining a LAN-local Qdrant index with Ollama embeddings. Indexing, staleness checks, and incremental updates happen transparently, so users can query code by meaning without managing collections, chunks, or hashes.

An MCP server that provides long-term memory and semantic search using Qdrant and OpenAI embeddings, with tools for storing, searching, and managing knowledge.

A provider-neutral MCP server that provides tools for managing skills, agents, references, and artifact memory in a local workspace.

An MCP server that enables saving, retrieving, and managing research content using ChromaDB vector storage and semantic search.

MCP server for managing and retrieving reusable SQL templates with semantic search, enabling a workflow of generation, retrieval, and templating.

Exposes a knowledge-management API as Model Context Protocol tools for saving, searching, ingesting, managing goal graphs and agent sessions, and coordinating relays. A curated default toolset keeps connection overhead low while loading the full tool set on demand.

Provides managed Apache Solr search as MCP tools, enabling hybrid retrieval, server-side GPU embeddings, document indexing, and grounded RAG answers without managing infrastructure.

Enables AI agents to gain persistent, structured memory that is semantically searchable and graph-traversable, allowing them to store and query records and relationships without managing embeddings or schemas.

Enables managing and controlling AnythingLLM instances through MCP, providing tools to list, create, and delete workspaces, retrieve system environment settings, and get vector counts.

Enables AI agents to connect to a LightRAG knowledge-graph server over HTTP, exposing 33 tools for querying, managing documents, and editing knowledge graph entities and relations with proper authentication and error handling.

MCP server for managing and searching multi-tenant knowledge bases backed by SQLite with FTS5, enabling AI agents to persist and retrieve content via full-text search.

Connects AI agents to Conduit data streams for ingesting events, querying data, and managing schemas. It enables seamless integration between services and agents with real-time forwarding and automated schema detection.

Enables persistent long-term memory for Claude Code across sessions, with tools for storing, searching, and managing facts, knowledge graphs, and procedural lessons.

Provides tools for storing, searching, and managing memories with knowledge-graph enriched context via Hydra DB's agentic memory.

Enables AI agents to interact with BeeBoo's human-in-the-loop infrastructure for managing knowledge bases, requesting human approvals, and tracking work requests. It provides tools for semantic search, authorization workflows, and task creation across platforms like Claude and Cursor.

Enables seamless integration with Convolut Context Bank for AI-powered context management, search, consolidation, and export operations. Provides 11 powerful tools for creating, managing, and analyzing contexts with semantic search and automated planning capabilities.

A Model Context Protocol server for managing prompt templates as markdown files with YAML frontmatter, allowing users and LLMs to easily add, retrieve, and manage prompts.

Enables hybrid dense and BM25 search over a markdown vault, with tools for reading, writing, grepping, and managing notes through any MCP-compatible client.

Enables storing and querying memories in a Neo4j knowledge graph with automatic entity extraction. Supports adding episodes, searching entities and relationships, and managing graph data through natural language.

Powerful Model Context Protocol server for managing Typebot chatbots, enabling operations like authentication, creating, updating, listing, and publishing bots through a standardized JSON interface.

An MCP server for querying and managing LlamaIndex documents stored in Qdrant vector databases, with automatic embedding model detection and extensive tools for search, retrieval, and collection management.

A comprehensive MCP server for managing AI-assisted Dungeons & Dragons campaigns, featuring tools for character sheets, combat tracking, and world-building. It enables players and DMs to interact with 5e game mechanics and query personal PDF rulebooks using RAG capabilities.

A Model Context Protocol server that enables AI models to interact with SourceSync.ai's knowledge management platform for managing documents, ingesting content from various sources, and performing semantic searches.

Unified MCP server combining dev, graph, docs, and kit tools for Lamatic.ai. Enables managing orgs/projects/flows, executing flows, querying docs via RAG, and browsing AgentKit contributions.

Enables MCP clients to administer a local LM Studio instance: loading, estimating, downloading, configuring and unloading models, running chat and embedding inference, diagnosing the server, engines and hardware, and managing MCP connectors with conflict-safe configuration. It also builds a local document index supporting citation-checked search and answers grounded in TXT, Markdown, PDF and DOCX files.

Enables interaction with Zotero libraries for searching, managing collections, items, tags, and attachments, plus optional semantic search across PDFs via local embeddings.

Combines a knowledge graph with RAG (Retrieval-Augmented Generation) capabilities for semantic code indexing and search. Enables creating entity relationships, managing observations, and performing semantic searches across indexed codebases.