Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
/plugin marketplace add n24q02m/claude-pluginsnpx agentmods add plugins/n24q02m/claude-plugins/mnemo-mcpgit clone --depth 1 https://github.com/n24q02m/claude-pluginsGrade A, and why
mnemo-mcp scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
100% identical to mnemo-mcp — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
What it actually says
{
"name": "mnemo-mcp",
"description": "Persistent AI memory — store, search, and recall knowledge across sessions",
"version": "2.8.0",
"userConfig": {
"JINA_AI_API_KEY": {
"type": "string",
"title": "Jina AI API key (optional)",
"description": "Optional cloud reranker key. https://jina.ai/api-dashboard/",
"sensitive": true,
"required": false
},
"GEMINI_API_KEY": {
"type": "string",
"title": "Gemini API key (optional)",
"description": "Optional cloud embedding + LLM. Free tier available. https://aistudio.google.com/apikey",
"sensitive": true,
"required": false
},
"OPENAI_API_KEY": {
"type": "string",
"title": "OpenAI API key (optional)",
"description": "Optional cloud embedding + LLM (lower priority than Gemini). https://platform.openai.com/api-keys",
"sensitive": true,
"required": false
},
"COHERE_API_KEY": {
"type": "string",
"title": "Cohere API key (optional)",
"description": "Optional cloud embedding + reranking. https://dashboard.cohere.com/api-keys",
"sensitive": true,
"required": false
}
},
"author": {
"name": "n24q02m",
"url": "https://github.com/n24q02m"
},
"homepage": "https://github.com/n24q02m/mnemo-mcp",
"mcpServers": {
"mnemo": {
"command": "uvx",
"args": [
"--python",
"3.13",
"mnemo-mcp"
],
"env": {
"MCP_TRANSPORT": "stdio",
"JINA_AI_API_KEY": "${user_config.JINA_AI_API_KEY}",
"GEMINI_API_KEY": "${user_config.GEMINI_API_KEY}",
"OPENAI_API_KEY": "${user_config.OPENAI_API_KEY}",
"COHERE_API_KEY": "${user_config.COHERE_API_KEY}"
}
}
}
}
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 2d ago First seen · 58 lines scan A c029e83f6f0f
mnemo-mcp is a plugin published in the GitHub repository n24q02m/claude-plugins (3 stars, last pushed 2d ago), licensed Apache-2.0. Its token cost is not measured: this kind of file is read by the harness, not the model. A static security scan graded it A with 0 findings. It is 100% identical to mnemo-mcp, differing in 0 lines, and is treated as a copy.
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