Interop

Interop – A Unified Command & AI Integration Layer

Interop is a command‑line framework that unifies project organization, custom tooling, and AI assistant integration into a single, context‑aware interface. By treating each repository or workspace as an Interop project, developers can attach metadata, command definitions, and validation rules that the tool enforces automatically. When a developer runs , Interop resolves the appropriate project, injects any required arguments, and executes the underlying shell or script with full awareness of that project's environment. This eliminates the need to remember disparate , , or ad‑hoc scripts, streamlining repetitive workflows across heterogeneous codebases.

The server’s most compelling feature is its native Model Context Protocol (MCP) support. Interop can spin up multiple MCP servers—each bound to a distinct domain or set of capabilities—and expose the same command catalog to AI assistants such as Claude or GPT. Commands can be tagged with a field, ensuring that only the relevant server advertises them. The MCP metadata includes descriptive text, argument schemas, examples, and even versioning, giving the assistant a rich context for precise execution. This tight coupling allows an AI to invoke with the correct parameters, or prompt a developer for missing values, all while maintaining strict project boundaries.

Key capabilities of Interop include:

• Project Management – Declarative TOML definitions that validate paths, ensure projects live under , and automatically surface command lists.
• Dynamic Command Loading – Commands can be sourced from multiple directories, with precedence rules that let shared libraries override local ones.
• Remote Configuration Sync – Fetch and merge configuration from Git repositories, complete with conflict resolution, so teams keep a single source of truth.
• Cross‑Platform Support – Works natively on Linux, macOS, and Windows, abstracting away shell differences.
• Rich AI Metadata – Every command carries a description, argument types, examples, and optional flags that inform the AI’s execution strategy.

Typical use cases include:

• CI/CD Pipelines – Exposing build, test, and deploy commands to an AI that can trigger them on demand or as part of a conversation.
• Onboarding – New contributors ask the AI how to set up or run tests; Interop returns a precise, context‑aware command.
• Domain‑Specific Tooling – Teams can create separate MCP servers for front‑end, back‑end, and infrastructure, preventing accidental cross‑domain execution.
• Automation – Developers schedule routine tasks via AI prompts, while Interop guarantees they run in the correct project context.

By bridging local tooling with AI assistants through a well‑structured, metadata‑rich protocol, Interop transforms ad‑hoc command execution into a disciplined, discoverable workflow. Developers benefit from reduced cognitive load, fewer errors, and the ability to harness AI’s conversational power without compromising on project isolation or security.