CodeScene MCP Server

The CodeScene MCP Server exposes CodeScene’s Code Health analysis as local AI-friendly tools.

This server is designed to run in your local environment and lets AI assistants (like GitHub Copilot, Cursor, Claude code, etc.) request meaningful Code Health insights directly from your codebase. The Code Health insights augment the AI prompts with rich content around code quality issues, maintainability problems, and technical debt in general.

The repository also includes a downloadable set of public agent skills in skills/ for teams that want to reuse CodeScene MCP workflows in their own agentic pipelines.

Getting Started with CodeScene MCP

Want AI to perform the setup? Start with skills/installing-and-activating-codescene-mcp/SKILL.md.

1. Setting up Authentication to the MCP Server — see Authentication.
2. Install the MCP Server using one of the installation options below.
3. Add the MCP Server to your AI assistant. See the detailed instructions for your environment in the installation guide.
4. Copy the agent guidance that matches your license into your repository: AGENTS-full.md for CodeScene Core users, AGENTS-standalone.md for standalone license users, or .amazonq/rules for Amazon Q. Also copy any relevant public skills for reusable workflow prompts.
5. Explore the available tools to see what the MCP Server can do and which tools are available for your license.

Installation

Choose the installation method that works best for your platform.

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Use Cases

[!TIP] Watch the demo video of the CodeScene MCP.

[!NOTE] CodeScene MCP comes with a set of example prompts, agent guidance files to capture the key use cases, and a downloadable set of public skills. Copy the agent guidance that matches your license — AGENTS-full.md for CodeScene Core users or AGENTS-standalone.md for standalone users — and any relevant skills to your own repository.

With the CodeScene MCP Server in place, your AI tools can:

Safeguard AI-Generated Code

Prevent AI from introducing technical debt by flagging maintainability issues like complexity, deep nesting, low cohesion, etc.

Uplift Unhealthy Code for AI Readiness

AI refactoring quality improves when code is modular and easy to reason about. The MCP server gives your assistant concrete guidance to get there:

• run focused Code Health reviews,
• identify the specific design issues to address,
• refactor in small, measurable steps, and
• verify progress with updated Code Health scores.

This workflow works with MCP alone and is often enough to safely improve legacy code.

Make Targeted Refactoring

AI tools can refactor code, but they lack direction on what to fix and how to measure if it helped.
The Code Health tools solve this by giving AI assistants precise insight into design problems, as well as an objective way to assess the outcome: did the Code Health improve?

Understand Existing Code Before Acting

Use Code Health reviews to inform AI-driven summaries, diagnostics, or code transformations based on real-world cognitive and design challenges, not just syntax.

Review MCP Safeguard Impact

Ask your AI assistant to show your MCP usage overview to see Code Health uplifts, prevented degradations, and the scope and quality of your latest 250 MCP events. Supported clients display an interactive dashboard; other clients receive a Markdown summary. This API-connected feature requires a CodeScene Core account and is not available with a standalone license. See show_mcp_usage_overview for details.

Frequently Asked Questions

Building from Source

The MCP server is written in Rust. To build from source:

cargo build --release

The binary is produced at target/release/cs-mcp.

For more details, see:

• Building a static executable locally
• Building a static Linux executable with musl
• Building the Docker image locally