Prompt Decorators MCP Server

Prompt Decorators is a Model Context Protocol (MCP) server that brings a formal, composable approach to prompt engineering. By treating prompts as first‑class objects that can be annotated with lightweight “decorators,” the server allows developers to apply consistent, reusable behavior modifiers—such as reasoning steps, output formatting, or step‑by‑step instructions—to any LLM request. This abstraction removes the ad‑hoc, copy‑paste style of current prompt tuning and replaces it with a declarative syntax that is both human‑readable and machine‑interpretable.

At its core, the server implements the Prompt Decorators Specification v1.0, a language‑agnostic standard that defines decorator syntax, semantics, and extension hooks. The MCP interface exposes a single resource: , which accepts a raw prompt string and a list of decorator identifiers. The server processes the decorators in order, transforms the prompt accordingly, and forwards the enriched prompt to the underlying LLM. This workflow keeps the model agnostic while giving developers fine‑grained control over inference behavior without modifying application code.

Key features include a registry of 140+ pre‑built decorators covering common use cases such as , , and . The decorator system is extensible; custom decorators can be registered via a simple Python API and then exposed through MCP, enabling teams to encode domain‑specific guidelines or compliance rules. The server also provides diagnostic metadata—such as applied decorator order and transformation logs—allowing developers to audit prompt flows and debug subtle behavior changes.

In practice, Prompt Decorators is invaluable for building AI‑powered applications that require consistent output styles or structured reasoning across multiple models and environments. For example, a data‑analysis tool can enforce on all queries to ensure reproducible explanations, while a content‑generation service can apply to guarantee consistent formatting. Because the server operates via MCP, it integrates seamlessly with desktop assistants like Claude Desktop, chatbots, or custom inference pipelines, eliminating the need for bespoke prompt‑handling code in each client.

Overall, Prompt Decorators delivers a standardized, reusable language for shaping LLM behavior. By centralizing prompt transformations in an MCP server, developers gain a single source of truth for prompt policies, reduce duplication, and improve maintainability across complex AI workflows.