Research intelligence for AI coding agents. 2M+ CS papers with evidence and tradeoffs.
Config is the same across clients — only the file and path differ.
{
"mcpServers": {
"ai-paperlantern-code": {
"command": "<see-readme>",
"args": []
}
}
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Research intelligence for AI coding agents. 2M+ CS papers with evidence and tradeoffs.
No automated test available for this server. Check the GitHub README for setup instructions.
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Research intelligence that makes your AI coding agent smarter - one command setup.
Paper Lantern gives your AI coding agent access to 2M+ CS research papers - the right technique for your problem, with tradeoffs, benchmarks, and implementation guidance.
npx paperlantern@latest
That's it. Pick your agents, log in, and Paper Lantern is configured.
Prefer to set up manually, or using a client not listed above? See paperlantern.ai/docs for per-agent config snippets and the raw MCP endpoint.
The setup CLI:
Once configured, your agent gets these tools:
| Tool | What it does |
|---|---|
explore_approaches | Survey 4-6 approach families with evidence and tradeoffs |
deep_dive | Investigate one technique in depth - implementation, hyperparameters, failure modes |
compare_approaches | Side-by-side comparison of 2-3 candidates |
check_feasibility | GO / PROTOTYPE / RECONSIDER verdict given your constraints |
give_feedback | Tell us what helped and what didn't |
Paper Lantern activates when your agent is making technical decisions where research evidence could improve the outcome - choosing between algorithms, architectures, or techniques.
It does not activate for syntax questions, library lookups, debugging, or general programming tasks.