mcpindex.aiv0
← Index

io.github.rog0x/perf

io.github.rog0x/perf·v1.0.2·Memory & RAG
Quality Score
79
/100

Benchmark, memory, Big O analysis for AI agents

Repository →
§01  Install
Claude Desktop (claude_desktop_config.json)
{
  "mcpServers": {
    "perf": {
      "command": "npx",
      "args": [
        "-y",
        "@rog0x/mcp-perf-tools"
      ]
    }
  }
}
Cursor (.cursor/mcp.json)
{
  "mcpServers": {
    "perf": {
      "command": "npx",
      "args": [
        "-y",
        "@rog0x/mcp-perf-tools"
      ]
    }
  }
}
Cline (cline_mcp_settings.json)
npx -y @rog0x/mcp-perf-tools
§03  MCP Quality Score  ·  methodology
freshness
24
completeness
5
installability
25
documentation
15
stability
10
§04  Alternatives in Memory & RAG
DoneThat
ai.donethat/donethat

Privacy-first work tracking with summaries, reports, coaching, and AI-ready long-term memory.

Fodda Knowledge Graphs
ai.fodda/mcp-server

Expert-curated knowledge graphs for AI agents — PSFK Retail, Beauty, Sports and more.

ai.meminal/meminal
ai.meminal/meminal

Memory for deep conversational context across any platform