MCPRules

Overview

MCPRules is a dedicated Model Context Protocol server that centralizes the management of programming guidelines and coding standards. By exposing these rules as MCP tools, it lets AI assistants—such as Claude or other MCP‑enabled clients—query and enforce consistent coding practices across a team’s entire codebase. The server solves the common problem of fragmented style guides, enabling developers to maintain a single source of truth that can be shared, versioned, and automatically referenced by tooling.

At its core, MCPRules offers a lightweight rule‑management API. Rules are stored in plain Markdown files and can be sourced locally or from a GitHub repository, giving teams flexibility to keep guidelines in sync with their source control. The server provides two primary tools: Get Rules, which returns all rules or filters them by category, and Get Categories, which lists every available rule group. This design keeps the interface simple while still allowing sophisticated queries, such as fetching only language‑specific or project‑management rules.

Key capabilities include structured rule definitions with categories and key‑value pairs, robust filtering, and integration with popular editors like VSCode through the “Cline” extension or Claude Desktop. Developers can configure environment variables to point the server at a local rules file or a private GitHub repo, and the server automatically serves updated guidelines whenever the underlying Markdown changes. The modular storage approach means you can mix local and remote rules, ensuring that core principles remain on a shared repository while project‑specific tweaks stay local.

Real‑world scenarios for MCPRules abound. A large organization can publish a corporate style guide that all developers pull from the server, guaranteeing consistency across microservices. An open‑source project can host its guidelines on GitHub and let contributors query them via an AI assistant during pull requests, reducing the cognitive load of remembering formatting rules. Even solo developers benefit by keeping personal coding conventions in a single, version‑controlled file that their AI tools can reference when generating or refactoring code.

Integrating MCPRules into an AI workflow is straightforward: once the server is running, clients add it to their MCP configuration. The assistant can then call Get Rules to retrieve the current guidelines, use them to validate generated code, or prompt developers with reminders. Because rules are served as plain text, they can be parsed into any format the assistant needs—JSON, structured prompts, or even fed directly into a language model’s context window. This tight coupling between rule data and AI generation makes MCPRules an invaluable asset for teams seeking to enforce coding standards without manual oversight.