What are Prompt Decorators?
Prompt Decorators are composable annotations (e.g., `+++Reasoning(depth=comprehensive)`) that modify LLM behavior without rewriting the core prompt.
@synaptiai
基本信息
A standardized framework for enhancing how LLMs process and respond to prompts through composable decorators, featuring an official open standard specification and Python reference implementation. Claude Code plugin and MCP server integration.
使用下面的配置,将此服务器添加到你的 MCP 客户端。
{
"mcpServers": {
"prompt-decorators": {
"command": "python",
"args": [
"-m",
"prompt_decorators",
"verify"
]
}
}
}工具列表暂未提供
当前收录暂时没有可展示的工具列表,不代表服务没有工具。请查阅项目文档了解支持的能力。
工具是从 README 中自动提取的。维护者可以在 ## Tools 标题下列出工具,即可填充这部分内容。
Prompt Decorators is a comprehensive framework that standardizes how prompts for Large Language Models (LLMs) are enhanced, structured, and transformed. It includes both an official specification and a Python reference implementation, plus a Model Context Protocol (MCP) server that enables decorator functionality in tools like Claude Desktop.
Install the package via pip install prompt-decorators (or with [mcp] for MCP integration). Load decorator definitions, create decorator instances with parameters, and apply them to prompts. The MCP server can be run alongside Claude Desktop for on‑the‑fly decorator usage.
Prompt Decorators are composable annotations (e.g., `+++Reasoning(depth=comprehensive)`) that modify LLM behavior without rewriting the core prompt.
Run `pip install prompt-decorators`. For MCP integration, use `pip install "prompt-decorators[mcp]"`.
The MCP server enables prompt decorator functionality in tools like Claude Desktop, allowing users to apply decorators directly within those applications.
Yes, the specification is platform‑agnostic. The Python implementation and MCP server work with any LLM that accepts prompt modifications.
Decorators are defined in a standardized JSON registry and loaded at runtime. The package includes a comprehensive library of 140+ built‑in decorators.

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