APILinter MCP Server

The APILinter MCP Server is a specialized bridge that connects large‑language models (LLMs) to RESTful API linting services via the Model Context Protocol. It addresses a common pain point for developers: integrating automated API quality checks into conversational AI workflows without exposing raw HTTP endpoints or writing custom adapters. By speaking the MCP language, Claude and other assistants can request linting operations as first‑class tools, receiving structured responses that can be fed directly into design reviews or continuous integration pipelines.

At its core, the server implements a robust, stateless MCP interface that exposes several linting‑centric tools. These include , which checks an OpenAPI or Swagger file against a configurable rule set, and , which retrieves the available linting rules. Additional helpers such as and provide quick analytics, while offers a prompt‑based review that can be triggered by an AI assistant. The server’s design ensures that every tool invocation is retryable and guarded by a circuit breaker, so transient network hiccups or downstream linter outages do not derail the assistant’s workflow.

Key capabilities go beyond simple tool exposure. Structured logging and metrics collection enable operators to monitor usage patterns, while health checks keep the service self‑diagnostic. The Streamable HTTP transport model allows large validation results to be streamed back incrementally, keeping latency low even for complex specifications. Although some features such as rate limiting and API documentation are planned, the current feature set already delivers a production‑ready integration point for AI‑driven API development.

Developers can leverage the server in a variety of scenarios. In an automated design‑review pipeline, Claude could ask the MCP server to validate a newly drafted OpenAPI file and then summarize violations in natural language. During pair‑programming sessions, the assistant can fetch rule categories on demand to explain why a particular pattern is discouraged. Continuous integration systems can trigger the tool as part of a pre‑commit hook, ensuring that every change passes linting before merging. Because the MCP interface is stateless and fully typed, integrating it into existing CI/CD tooling or custom IDE extensions requires minimal effort.

What sets this MCP server apart is its focus on API linting as a first‑class AI service. By combining the expressive power of MCP with domain‑specific tools, it turns routine quality checks into conversational actions. This reduces friction for developers who want to embed rigorous API standards directly into their AI assistants, leading to faster feedback loops and higher‑quality APIs.