Ampersand MCP Server

Overview

The AI CLI is a lightweight, cross‑platform console utility that bridges the gap between AI assistants and everyday development workflows. By exposing a set of pre‑built tools—such as filesystem, Git, and GitHub issue management—the server enables AI agents to interact with a project’s codebase, version control history, and issue tracker directly from the terminal. This eliminates the need for custom integrations or manual API calls, allowing developers to focus on higher‑level problem solving while the CLI handles the plumbing.

At its core, the tool solves a common pain point: how to give an AI assistant contextual access to a project’s state without compromising security or requiring complex setup. Developers can simply point the CLI at a directory, configure an LLM provider (e.g., OpenRouter), and supply a natural‑language prompt. The AI then runs actions such as creating new repositories, generating changelogs, or labeling GitHub issues by calling the appropriate MCP endpoints. The CLI also supports batch operations like summarizing large texts or generating release notes, turning routine documentation tasks into quick, AI‑driven commands.

Key features include:

• Modular toolset – Choose from filesystem, Git, or GitHub issue/label tools to tailor the AI’s capabilities.
• Provider‑agnostic LLM integration – Switch between OpenAI, OpenRouter, or other models by specifying the provider and model name.
• Prompt‑file support – Feed multi‑line instructions via a markdown file, enabling complex workflows such as auto‑labeling or issue triage.
• Environment variable configuration – Securely inject API keys and tokens (e.g., , ) without hardcoding credentials.
• Cross‑platform .NET global tool – Install once and run on any machine that supports the .NET runtime, ensuring consistent behavior across development environments.

Typical use cases span the entire software delivery pipeline. In a CI/CD context, an AI agent can automatically generate release notes from commit history or scaffold new repositories with boilerplate code. During feature development, the CLI can analyze a branch’s diff and suggest relevant labels or pull‑request titles. For documentation, the tool can summarize long technical articles or generate code comments in bulk, saving developers hours of manual effort.

Integration with AI workflows is seamless: the CLI acts as an MCP server, exposing its capabilities to any LLM that understands the Model Context Protocol. An assistant can invoke these tools as part of a larger chain, combining natural‑language reasoning with concrete actions. Because the server is built on top of .NET, developers can extend it with custom tools or plug it into existing automation pipelines without re‑implementing the underlying protocol logic.

In summary, AI CLI empowers developers to harness the full potential of LLMs in their day‑to‑day tasks, turning a terminal into an intelligent assistant that can read, write, and modify codebases, version control states, and issue trackers—all while keeping sensitive credentials out of the code.