EditorConfig MCP Server

The EditorConfig MCP Server is a lightweight, protocol‑compliant service that automatically enforces style rules on source files. It was created to solve a common friction point in AI‑assisted development: code generators often introduce subtle formatting errors—trailing whitespace, inconsistent line endings, or misaligned indentation—that trigger linters and waste valuable AI cycles. By acting as a proactive formatting gatekeeper, the server ensures that any file produced by an AI assistant or written manually is immediately brought into compliance with the project’s established style guide, eliminating the need for post‑generation lint passes.

At its core, the server exposes two formatting tools: for a single path and for glob patterns. These endpoints accept minimal JSON payloads, validate them against a strict schema, and return concise success responses that include the affected file paths and byte counts. The implementation is stateless, meaning each request is independent, which simplifies scaling and integration with any MCP‑compatible assistant. Built on top of a robust rate‑limiting layer, the service guarantees predictable performance even under bursty workloads, while its OpenAPI 3.0 specification makes discovery and client generation effortless.

Key capabilities include:

• MCP‑compliant design: Adheres to all protocol standards, ensuring seamless discovery via and consistent error handling.
• JSON Schema validation: Guarantees that every request conforms to the expected shape, reducing runtime errors.
• Security hardening: Path validation protects against directory traversal attacks; the server runs on a configurable port to avoid clashes with other development services.
• Self‑documenting API: The endpoint provides a machine‑readable contract that can be consumed by IDEs or custom tooling.

In practice, developers use the server to integrate formatting into their AI workflows. For example, a Claude Code user can add the service with , after which any generated file is automatically formatted before it reaches the developer’s workspace. The same pattern works for other MCP‑compatible assistants, allowing teams to enforce consistent code style across multiple AI tools without manual intervention.

Because the service is lightweight, stateless, and fully compliant with MCP best practices, it offers a unique advantage: teams can run it locally or in CI pipelines without changing their existing tooling stack. The result is cleaner, lint‑free code from the outset and a smoother collaboration between human developers and AI assistants.