Prompt MCP Server

A framework for defining, managing, and applying prompt decorators to enhance interactions with LLMs

Local serverstdioPython

What is the Prompt MCP server?

Prompt MCP server exists for a simple reason — assistants are far more useful when they can act on Prompt directly instead of describing what you should do. A framework for defining, managing, and applying prompt decorators to enhance interactions with LLMs.

What you get

Prompt Decorators introduces a standardized annotation system inspired by software design patterns that allows users to modify LLM behavior through simple, composable "decorators." By prefixing prompts with annotations like +++Reasoning, +++StepByStep, or +++OutputFormat, users can consistently control how AI models process and respond to their requests across different platforms and implementations.

  • 📚 Registry-based decorator management — Centralized registry of decorators with metadata
  • ✅ Parameter validation and type checking — Robust validation of decorator parameters
  • 🔢 Decorator versioning — Support for semantic versioning of decorators
  • 🔄 Compatibility checking — Verification of decorator compatibility
  • 📝 Documentation generation — Automatic generation of documentation for decorators
  • 🧩 Dynamic loading — Runtime decorator loading from definition files

What the assistant can call

Once Prompt is connected, these are the calls the assistant has available:

Setting it up

The server ships on PyPI as prompt-decorators, so your MCP client can launch it on demand — there is no separate build step. Add the server block to your client's configuration, restart it, and the tools register themselves.

Choosing this one

This sits in the knowledge and memory group, where several servers overlap in what they claim to do but differ sharply once you actually set them up. Prompt's toolset — Roadmap, Installation — is a fair guide to whether it matches your workflow. It is maintained by Daniel Bentes; worth a glance at recent repository activity before you build anything load-bearing on it.

This entry was verified against Prompt's own documentation before publication; SyncDev keeps the directory reviewed rather than auto-generated.

Before you rely on it

  • It runs with your machine's permissions. That is convenient and also the reason to think about what you point it at before you approve a tool call.
  • MCP clients confirm each tool call by default. Leave that on until you have watched what the prompt mcp server does with a few real requests.

Available tools

ToolWhat it does
RoadmapThe roadmap for this project is outlined in the [ROADMAP](https://synaptiai.github.io/prompt-decorators/roadmap/) file.
InstallationYou can install the package from PyPI [https://pypi.org/project/prompt-decorators/](https://pypi.org/project/prompt-decorators/):

How to install the Prompt MCP server

{
  "mcpServers": {
    "prompt-decorators": {
      "command": "uvx",
      "args": ["prompt-decorators"]
    }
  }
}

Add to claude_desktop_config.json, then restart Claude Desktop.

Example prompts to try

  • Use Prompt to Roadmap.
  • Use Prompt to Installation.

Frequently asked questions

It connects Prompt to MCP-compatible AI assistants such as Claude and Cursor, exposing 2 tools (Roadmap, Installation) that the assistant can call on your behalf. Instead of copying data back and forth by hand, the assistant works with Prompt directly.