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Query live architecture models, fetch target patterns, and check files for compliance.
Copy the install command and let the AI configure it · recommended for beginners
Please install the "io.github.einvoice-dev1/archsteer" MCP server from askskill: Run: claude mcp add 'io-github-einvoice-dev1-archsteer' -- uvx archsteer
Before letting an AI agent change code, developers can query the live architecture model and target patterns to avoid implementations that conflict with the existing design. They can then check related files to catch deviations early.
When multiple teammates or agents work on different modules, they can use it to retrieve shared target patterns and check whether files follow architecture governance requirements. This helps reduce structural inconsistency.
It is used for architecture governance for AI agents. Its capabilities include querying the live architecture model, retrieving target architecture patterns, and checking files. It is suitable as a guidance and validation layer when generating or modifying code.
The provided material does not include installation steps, runtime requirements, or key information. See the source repository for exact prerequisites.
Based on the description, it does more than inspect files by also using a live architecture model and target patterns for governance-related checks. For more detailed differences, see the source repository.
Keep AI coding agents architecture-aware, verified, drift-checked, and safer over long tasks.
Plan multi-agent architectures, agent roles, and code collaboration strategies.
Generate architecture docs, ADRs, and review outputs for software design decisions.
Establish enterprise governance, delivery standards, and assurance for AI coding assistants.
Automatically save and restore AI sessions to preserve architecture context and decisions.
Analyze codebase architecture, inspect dependencies, and generate interactive visual diagrams.