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Adds ephemeral hybrid memory to MCP editors for searchable chat and code context.
This MCP tool appears to provide ephemeral memory and search locally, with no required secrets and no declared remote endpoints. Overall risk is relatively low based on the available facts, but caution is still warranted because it comes from a third-party registry, has low community adoption, and lacks detailed documentation.
The materials explicitly state that no keys or environment variables are required. No API tokens, account credentials, or other sensitive secrets are requested, so credential leakage and abuse risk appears low.
No remote endpoints are declared in the materials, and the provided checks do not indicate a network target. Based on the available facts, there is no evidence of user data being sent to external services.
The system has flagged this tool as executes-code, indicating that it can run code or processes locally. This is a normal MCP capability, but the documentation does not define execution boundaries or system privilege scope, so the runtime environment should be assessed carefully.
It claims to save and search conversation and code context and perform automatic context injection, which implies handling user session content and code-related data. Although described as ephemeral, the materials do not specify storage location, retention period, or file access scope.
Positive factors include being open source under Apache 2.0, which makes source review possible. However, it is distributed via a third-party registry, shows 0 stars, has unknown maintenance status, and lacks a README, so supply-chain transparency and maturity are limited.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "N3MemoryCore MCP — Lite (Ephemeral)" yet — see the docs or source repo.
Save the key debugging findings, root causes, and next steps to memory; when I reopen the project later, retrieve and inject the relevant context first.
The tool stores the current development context and later retrieves relevant records so the AI can continue seamlessly.
Search past conversations, code snippets, and design decisions about the authentication middleware refactor, then summarize why that approach was chosen.
It returns matched historical context and a concise summary of the reasoning behind key technical decisions.
Save the user interview findings, feature hypotheses, and document summaries, and prioritize retrieving them in future roadmap discussions.
The tool records product research context and automatically surfaces the most relevant references in later discussions.
Provide persistent local semantic memory for MCP tools to store and search notes.
Lightweight vector memory for AI agents to store, search, and delete memories.
Help MCP clients remember preferences and retrieve key context across chats.
Give AI agents persistent long-term memory with hybrid semantic and keyword search.
Give AI agents persistent memory and semantic retrieval across conversations.
Enable MCP clients to remember users across chats with vector search.