"MCP server that provides both prompts and resources" 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.

MCP server that demonstrates the Resources feature by exposing static and dynamic resources, including contact data and personalized greetings, through MCP.

LLM routing proxy that cuts API costs 60-90% by auto-selecting the cheapest capable model across OpenAI, Anthropic, and Google. Provides stats, config, and model comparison tools via MCP.

Enables AI assistants to register prompts for security verification, checking for hidden injections, data exfiltration patterns, and jailbreak attempts, then generate embed codes for displaying security badges on websites.

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

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.

A tutorial demonstrating how to build reusable, parameterized MCP prompt templates using FastMCP, including patterns for research, summarization, and code review.

A Model Context Protocol implementation for managing and serving AI prompts with a TypeScript-based architecture in a monorepo structure.

Lightweight MCP server for providing user prompts defined in Markdown with argument support.

A simple MCP server for utilizing prompts defined in YAML files.

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

Enables interaction with MongoDB databases through CRUD operations, aggregation, and schema discovery, with automatic field validation and ObjectId conversion.

MCP server for managing AI prompts with CRUD operations, categorization, and tagging. Enables users to store, organize, and retrieve their favorite prompts efficiently.

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.

Model Context Protocol server exposing system prompt files and summaries.

Relays a result screenshot to Slack so you can review a coding agent's output without opening the app.

Automatically generates and manages a prompts system for software projects, enabling persistent context for AI coding assistants through project scanning, requirement clarification, and module tracking.

Enables AI agents to break down complex tasks into manageable pieces using a structured JSON format with task tracking, context preservation, and progress monitoring capabilities.

Exposes generated CRUD services to LLM clients over the Model Context Protocol, providing tools for create, update, and delete actions plus read-only resources for listing and fetching records by id. It lets assistants like Claude, ChatGPT, and Cursor discover and manipulate the same data served by the project's REST endpoints without any extra glue code.

Enables LLMs to discover and invoke CRUD tools and read-only resources for generated services, allowing direct interaction with the same data exposed by the REST API.