"Executing Operations and Maintenance Tasks, Such as Deployment and Upgrades" matching MCP servers:

Provides a SQLite-backed MCP server that records agent memory as history, enabling explicit consolidation, source checks, revisions, narrative versions, and accountable forgetting.

Enables querying a modular RAG knowledge base via MCP, with hybrid retrieval, reranking, and document summary tools.

A multi-agent operations assistant that answers business questions by searching local documents and order records.

An MCP server exposing internal business operations as tools — task management (create, list, update status) and RAG-style semantic search over an internal knowledge base (leave, expense, and onboarding policies) that any MCP-compatible AI agent can call directly for grounded, non-hallucinated answers.

A multi-tool AI assistant system that uses Model Context Protocol to connect language models with various tools, including math calculations and weather information.

Exposes Dify applications (both Chatflow and Workflow) as MCP (Model Context Protocol) servers, allowing Claude and other MCP clients to directly interact with Dify apps through a standardized protocol.

A meta-MCP server that acts as a universal gateway, allowing users to discover and execute tools from thousands of other MCP servers through semantic search. It dynamically loads servers on demand and provides standardized functions for searching, discovering, and running tools across the entire MCP ecosystem.

Enables running multiple Claude prompts simultaneously in parallel with support for file contexts and output redirection to individual files.

Enables searching imported AI conversations locally across different providers from an MCP client, with read-only access to the archive.

CTX is a tool made to solve a big problem when chatting with LLMs like ChatGPT or Claude: giving them enough context about your project.

A Model Context Protocol server that provides safe, intelligent read/write access to NetBox instances, designed with safety-first principles for Large Language Model automation.

Provides a standardized protocol for tool invocation, enabling an AI system to search the web, retrieve information, and provide relevant answers through integration with LangChain, RAG, and Ollama.

Enables cloud and platform engineers to investigate AWS incidents by grounding responses in approved runbooks and operational evidence, with authenticated access, citations, observability, and human approval before any write action.

Unified MCP server for the AmorSaúde ecosystem, combining SQL query, semantic search, medical record similarity, audio transcription, and LLM-as-Judge evaluation tools for multiple healthcare agents.

Provides structured dealer brand data including inventory, promotions, reviews, and dealer profile via MCP tools, enabling LLMs to answer accurate brand-related queries.

An MCP server that provides custom prompts for guiding bot interactions, specifically to avoid using brackets in descriptions.

Exposes hybrid retrieval (dense embeddings + BM25 + RRF) and document operations (search, fetch, rerank) as MCP tools, using Qdrant and OpenAI embeddings for local or server mode.

A local RAG MCP server that indexes project documentation and code into ChromaDB for semantic search, supporting multiple tech stacks and deployment modes.

Enables AI assistants to interact with a Qdrant vector database by exposing collection, point, vector, payload, snapshot, search, recommendation, discovery, and observability operations as MCP tools.

Enables AI assistants to interact with Watson Discovery, listing projects and collections, and executing natural language queries.

Enables AI agents to store and recall persistent long-term memories across sessions using LanceDB, with semantic search, automatic linking, conflict detection, and maintenance tools.

A server implementation for interacting with Wikidata API using the Model Context Protocol, providing tools for searching identifiers, extracting metadata, and executing SPARQL queries.

Enables interaction with Google's NotebookLM via the Model Context Protocol to manage notebooks, sources, and research tasks. Users can create, query, and summarize content, as well as generate artifacts like audio overviews and slide decks using natural language.

A TypeScript-based MCP server that enables users to manage tasks through natural conversation with Claude. Features complete CRUD operations, priority management, tagging, search functionality, and intelligent productivity insights with robust Zod validation.

A Model Context Protocol server that enables AI assistants like Claude to interact with Zulip workspaces, supporting capabilities such as posting messages, listing channels, sending direct messages, and accessing conversation history.

Provides local-first, zero-LLM memory retrieval for AI agents like Claude Code and Codex, enabling verbatim recall of stored notes and context through tools such as fidelis_recall and fidelis_orient.

Enables MCP clients to query a local screen-memory journal through tools such as ask_memory, list_topics, read_topic, read_observation, and read_day_flow, returning cited captured excerpts with app, time, and document metadata.

Web scraping for AI agents: scrape, map, crawl and batch public pages into Markdown, tables and JSON, with an Evidence Record on every page. Blocked, empty and wrong pages come back as such, with the reason.

Facilitates two-stage reasoning processes using DeepSeek for detailed analysis and supports multiple response models such as Claude 3.5 Sonnet and OpenRouter, maintaining conversation context and enhancing AI-driven interactions.

A Model Context Protocol server that provides seamless integration with Trino and Iceberg, enabling data exploration, querying, and table maintenance through a standard interface.