"A server offering MCP prompts as primitives for task execution" matching MCP servers:

Enables creation, management, and templating of prompts through a simplified SOLID architecture, allowing users to organize prompts by category and fill in templates at runtime.

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.

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

An enhanced MCP server that fetches prompts from GitHub repositories with intelligent discovery, composition, and management features.

A Model Context Protocol server that generates prompts based on Git repository content, including a command to generate PR descriptions from diffs.

Enables management and invocation of AWS Bedrock managed prompts with support for variable substitution, streaming responses, batch processing, and multiple AI models including Claude, Titan, Llama, and Mistral.

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

Async MCP server for running long-running AI tasks with real-time progress monitoring, enabling users to start, monitor, and manage complex AI workflows across multiple models.

About Your task manager is the best agent memory you're not using. An agent-native context layer over your existing task app.

Enables hot-reloadable prompt templates with structured reasoning frameworks, quality gates, and multi-step chain workflows that can be version-controlled and executed immediately without restarts.

Improves AI agent task performance by providing task area segmentation, hierarchical RAG, and hybrid search capabilities.

Enables MCP-compatible agents to retrieve Chinese expert documents, prompt examples, and task skeletons before writing Chinese prompts.

Exposes task management (add, list, complete tasks) and document search (RAG) as MCP tools for AI agents.

Enables any MCP-compatible AI assistant to search, filter, and retrieve information from a local document collection using a hybrid search pipeline with vector, BM25, reranking, and LLM enrichment.

Provides an intelligent, graph-based memory system for LLM agents using the Zettelkasten principle, enabling automatic note construction, semantic linking, memory evolution, and autonomous graph maintenance with background optimization processes.

MCP for Azure DevOps Boards is a MCP server that lets your favourite AI browse, query and update Azure DevOps work items as if it were a project manager. Written in Rust and optimized for tokens usagem, It runs via stdio or HTTP mode and uses standard Azure authentication with az login.

A proof of concept MCP server for Apache OFBiz

Production-ready MCP server providing RAG, hierarchical memory, and 8+ tools for AI agents via the Model Context Protocol.

WordPress plugin that runs an MCP server on your own site: search and read published posts and pages, look up WooCommerce products, describe the site as schema.org types and query site figures, with drafts only when the site enables them. Signed-in access by default; anonymous access and OAuth are optional and off.

Enables asking questions and performing semantic searches against an Inkeep knowledge base. Deployable to Vercel with Next.js.