Bidirectional spec-to-code drift detection

Rust CLI and MCP server tool to detect undocumented exports and vanished symbols in CI.

Bidirectional Spec-to-Code Drift Detection

This tool provides a robust solution for maintaining API integrity by detecting discrepancies between your API specification and its actual implementation. Designed for developers working with complex systems, it ensures that your codebase remains synchronized with its documented interface, preventing unexpected breakages and improving overall stability. The system operates bidirectionally, meaning it checks for both additions to the code that are not in the spec, and removals from the code that are still expected by the spec.

What it Does

The Bidirectional Spec-to-Code Drift Detection tool is a command-line interface (CLI) and an MCP server component built in Rust. Its primary function is to identify "undocumented exports" – code elements present in your implementation but missing from your API specification – and "vanished symbols" – elements expected by your specification but no longer found in your codebase. This is particularly valuable within Continuous Integration (CI) pipelines, where it can automatically flag potential API drift before it impacts production.

Key Features

Rust CLI: A performant and reliable command-line tool for local and automated checks.
MCP Server: Integrates with your existing MCP server infrastructure for continuous monitoring.
CI Integration: Designed to be seamlessly incorporated into CI workflows to catch API drift early.
Bidirectional Detection: Identifies both undocumented additions and missing removals in your API.
Symbol and Export Tracking: Focuses on the precise identification of API surface changes.

Who it's For

This tool is essential for AI builders , software engineers , and DevOps professionals who are responsible for maintaining APIs, especially those with evolving codebases. It is particularly beneficial for projects that rely on strict API contracts, microservices architectures, or any scenario where maintaining a clear and consistent interface between different components is critical. If you are building or managing AI models and their associated APIs, ensuring spec-to-code alignment is paramount for reliable deployment and integration.