Qartez MCP by Kuberstar is a Model Context Protocol server, built with Rust, designed to empower AI assistants. It functions as a semantic code intelligence server, constructing and serving a knowledge graph of a codebase directly to AI agents. Distributed as a single Rust binary, it offers a local-first approach with no cloud dependencies or API keys. This platform enhances AI capabilities for code understanding, impact analysis, and architectural insights, supporting various popular AI clients and editors.

What does Qartez MCP connect to?

Qartez MCP connects directly to AI assistants and AI coding agents as a Model Context Protocol (MCP) server. Its core function is to build and serve a knowledge graph of a codebase, providing semantic code intelligence to these agents. While the specific transport protocols Qartez MCP supports are not explicitly stated, the Model Context Protocol generally utilizes stdio and SSE over HTTP. It integrates with many AI clients, including Claude Code, Gemini, Cursor, Zed, Continue.dev, Copilot CLI, Amazon Q, and Amp, among others.

Core features and tools

Qartez MCP provides a set of callable tools to AI agents for deep code interaction. These include qartez_impact for change analysis, qartez_diff_impact, and qartez_context. For code exploration, it offers qartez_hotspots, qartez_clones, qartez_boundaries, qartez_find, and qartez_outline. Project-level insights are available through qartez_project and qartez_wiki, alongside a general qartez_tools function. The system exposes either 21 or 37 MCP tools in total, enabling detailed semantic understanding of codebases.

Who is it for?

Qartez MCP is primarily intended for AI agents and AI coding agents that require deep semantic understanding of codebases. It is suitable for developers and organizations aiming to augment their AI tools with local-first code intelligence capabilities. Individuals can use it under a free license, while businesses require a commercial license. This server helps AI agents perform tasks like code navigation, impact analysis, and architectural assessments without external cloud dependencies. Skip this if your AI workflow does not involve direct interaction with a local code intelligence server.

How to get started

No credentials are required for Qartez MCP, as it operates with a local-first authentication model and has no cloud dependency, removing the need for API keys. The Model Context Protocol (MCP) generally supports stdio and SSE over HTTP for communication between the server and AI clients, providing standard interfaces for data exchange.

Use cases

• Code understanding and navigation: Enables AI agents to efficiently find symbol definitions and outline files, replacing traditional manual search operations.
• Impact analysis and risk assessment: Allows AI to determine the "blast radius" of changes and enforce modification guards on high-impact files.
• Code quality and architectural analysis: Identifies architecturally important files, detects structural code clones, and enforces architecture boundaries.

Qartez MCP provides AI agents with a powerful, local-first semantic code intelligence server. Its Rust-based architecture and direct integration with numerous AI clients offer a solid foundation for advanced code understanding and analysis. The system supports critical workflows from impact assessment to architectural enforcement, all without cloud dependencies.