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MCP Prompts is a prompt management and orchestration system for the Model Context Protocol (MCP). It provides system prompt templates, prompt injection controls, and multi-agent prompt composition for AI agent workflows.
# MCP Prompts ## Project Overview MCP Prompts is a prompt management and orchestration system for the Model Context Protocol (MCP). It provides system prompt templates, prompt injection controls, and multi-agent prompt composition for AI agent workflows. ## Integration Scope While not part of the core MIA IoT control system, MCP Prompts can enhance: - Agent decision-making in elevenlabs-agents - Natural language processing for voice commands - Multi-agent coordination in advanced deployments See [MIA](https://github.com/sparesparrow/mia) for the IoT core system. ## Tasks ### Phase 1: Prompt Storage & Management - [ ] Design prompt storage format (YAML/JSON) - [ ] Implement prompt versioning - [ ] Create prompt library structure - [ ] Add prompt tagging and categorization - [ ] Implement prompt validation ### Phase 2: MCP Integration - [ ] Create MCP server for prompt management - [ ] Implement prompt retrieval tools - [ ] Implement prompt composition tools - [ ] Add prompt injection detection ### Phase 3: Agent Integration - [ ] Connect with agent orchestration - [ ] Implement dynamic prompt selection - [ ] Add context-aware prompting - [ ] Multi-agent prompt coordination ## Status - **Maintenance Mode**: Support for existing MCP servers - **Recommended**: For new agent projects, focus on core MIA + voice agents - **Optional**: Use as enhancement for complex multi-agent scenarios ## See Also - [MIA - Lean IoT Assistant](https://github.com/sparesparrow/mia) - [ElevenLabs Agents - Voice Integration](https://github.com/sparesparrow/elevenlabs-agents) - [MCP Project Orchestrator](https://github.com/sparesparrow/mcp-project-orchestrator)
IMPORTANT: Fallow the rules in the './.rules.md' file
A clear, well-structured prompt dramatically improves the quality of AI-generated outputs. This guide outlines key principles and examples for writing effective prompts using the **RTCF** framework and other best practices.
<citation_instructions>If the assistant's response is based on content returned by the web_search, drive_search, google_drive_search, or google_drive_fetch tool, the assistant must always appropriately cite its response. Here are the rules for good citations:
description: Publishable Prompt Engineering skill package that compiles a user request into a ready-to-use high-quality Prompt, with support for diagnosis, module injection, debugging, and evaluation.
