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Give AI coding agents structured access to project architecture and decisions.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "ArchMemory MCP" yet — see the docs or source repo.
Use ArchMemory MCP to read this project's architecture, module boundaries, and rules, then tell me which module should contain a new payment feature and list the constraints I must follow.
A summary of the project structure, a recommended module location, and the relevant architectural rules or constraints.
Use ArchMemory MCP to find why this project chose its current authentication approach and summarize how that technical decision affects future development.
An explanation of the decision context behind the authentication approach and how it affects implementation, extension, or refactoring.
First use ArchMemory MCP to get the project rules and module information, then generate an implementation plan for a user profile API that fits the existing architecture.
Implementation guidance that follows project rules, including placement, dependencies, and key considerations.
Before using AI for coding assistance, developers can let it read the project's architecture, modules, and rules. This helps reduce suggestions that conflict with the existing structure and improves consistency.
When a team is preparing to restructure modules or refactor features, this tool can give AI access to technical decisions and boundaries first. That makes it easier to plan changes within existing constraints.
Developers can have AI read project rules first, then produce implementation guidance that better matches the current module design. This is useful for engineering projects with established architecture and conventions.
It is an MCP server that gives AI coding agents structured access to a project's architecture, rules, modules, and technical decisions. Its main purpose is to help AI understand and work within project context.
It is most relevant for developers and technical team members who use AI with engineering context. It is especially useful when AI coding assistants are used in complex codebases.
The provided material does not include installation steps, runtime details, or key requirements. For prerequisites and setup, see the source repository.
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