Documentation Index

Fetch the complete documentation index at: https://docs.leeroopedia.com/llms.txt Use this file to discover all available pages before exploring further.

Leeroopedia MCP

Give your AI coding agent access to curated ML/AI knowledge

$20 free credit on sign-up. That's plenty of searches, plans, and diagnoses. Skip the guesswork on your next fine-tuning run or inference deployment. No credit card required. Get your API key →

What is Leeroopedia?

Your ML & AI Knowledge Wiki. Learnt by AI, built by AI, for AI.

Expert-level knowledge across the full ML & AI stack: fine-tuning and distributed training, inference serving and GPU kernel optimization, building agents and RAG pipelines. 1000+ frameworks and libraries, all in one place.

This MCP server turns your AI coding agent (Claude Code, Cursor, Claude Desktop, ChatGPT, OpenAI Codex, ...) into an ML/AI expert engineer.

Browse the full knowledge base at leeroopedia.com.

Want to go end-to-end?

Leeroopedia gives your agent the knowledge. Kapso gives it the ability to act on it: research, experiment, and deploy. Together: a complete ML/AI engineer agent.

Connect to Your Agents

Use our hosted server for zero-setup. Just paste this URL into any MCP client that supports remote servers:

https://mcp.leeroopedia.com/mcp?token=kpsk_your_key_here

Or see the per-client guides below for detailed instructions (including local setup).

Set up with Claude Code Set up with Cursor Set up with Claude Desktop Set up with OpenAI Codex Set up with ChatGPT

Benchmarks

We measured the effect of Leeroopedia MCP on real ML tasks:

• ML Inference Optimization. Write CUDA/Triton kernels for 10 KernelBench problems. 2.11x geomean speedup vs 1.80x (+17%), with/without Leeroopedia MCP.

• LLM Post-Training. End-to-end SFT + DPO + LoRA merge + vLLM serving + IFEval on 8×A100. 21.3 vs 18.5 IFEval strict-prompt accuracy, 34.6 vs 30.9 strict-instruction accuracy, 272.7 vs 231.6 throughput.

• Self-Evolving RAG. Build a RAG service that automatically improves itself over multiple rounds. 45.16 vs 40.51 Precision@5, 40.32 vs 35.29 Recall@5, in 52 vs 62 min wall time.

• Customer Support Agent. Multi-agent triage system classifying 200 tickets into 27 intents. 98 vs 83 benchmark performance, 11s vs 61s per query.

Detailed results, analysis, and replication instructions for all 4 benchmarks

Available Tools

The server provides 8 agentic tools: search, plan, review, verify, diagnose, hypothesize, query hyperparameters, and retrieve pages.

See all 8 tools with parameters and usage

Quick Links

Connect in 2 minutes Connect in 2 minutes Connect in 2 minutes Connect in 2 minutes Connect in 2 minutes See the results All 8 tools explained