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Build semantic memory and structural code indexes for persistent AI project context.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "mcp-context-memory" yet — see the docs or source repo.
Build a semantic index for this local repository, use AST analysis to identify major modules, dependencies, and key entry files, and persist current architecture decisions and design constraints for future AI conversations.
A code structure overview, module relationship notes, and reusable persisted project memory.
Read the existing project memory and code index, then tell me which files, core functions, and middleware are involved in the user authentication flow, and explain the previously recorded design decisions.
An accurate mapping of relevant code plus explanations based on stored decisions and structure.
Scan this large project, organize module boundaries by domain, find the payment-related code paths, and summarize which context should be preserved long term for the AI assistant.
Domain-based module mapping, key code paths, and a recommended list of long-term context to preserve.
Give AI coding assistants persistent, structured project memory stored as local Markdown.
Build local codebase memory for AI agents with search and architecture insights.
Build persistent, semantically searchable memory for codebases via natural language queries.
Provide persistent local semantic memory for MCP tools to store and search notes.
Read, edit, and refactor code precisely with AST-based, token-efficient operations.
Provide persistent local memory for development context, decisions, and past discussions.