Why Cursor Users Need MegaLens
30+ models available, but ONE at a time. Cursor Auto routes each request to a single model. You get breadth of choice, not breadth of perspective.
Generation and review are fundamentally different problems. Same model writing and reviewing = conflict of interest. The author can't be the auditor.
Multiple specialists, structured disagreement. MegaLens runs multiple specialist engines simultaneously and structures their disagreements into findings.
"Access Is Not a Pipeline"
30 models available is not the same as 30 models working together.
Having a roster of engineers is not the same as running a code review where all of them read the same diff, argue, and defend conclusions to a senior judge.
Cursor Auto picks ONE model per request. MegaLens runs multiple engines simultaneously.
How It Works
Write code in Cursor normally.
Trigger MegaLens review via MCP: megalens_debate.
Multiple specialist engines debate: security, logic, performance.
GPT 5.4 judges the debate, filters disagreements, produces structured verdict.
Cursor applies fixes based on findings.
Setup
Quickest way: run the setup wizard. It detects Cursor and writes the config for you.
npx megalens-mcp setup
Detects Cursor, asks for your token, and writes the config below automatically. Or paste it yourself into .cursor/mcp.json:
{
"mcpServers": {
"megalens": {
"url": "https://megalens.ai/api/mcp",
"headers": {
"Authorization": "Bearer ml_tok_your_token_here"
}
}
}
}Get your token at megalens.ai/app/settings/mcp. Restart Cursor to activate. Source: github.com/megalens/mcp.
Your IDE model stays focused on writing code
When Cursor reviews code, it uses the same premium model (Sonnet, Opus) that writes your code. Every review prompt burns tokens from that expensive context window.
Without MegaLens
Cursor's premium model does both generation and review. Review prompts compete with coding context for the same token budget.
With MegaLens
Deep review shifts to cheaper specialist engines (Grok, DeepSeek, Gemini). Your IDE's model keeps its full context window for writing code.
From our case study: Cursor found 23 issues solo. MegaLens added 22 more for $0.22. 7 of those came from the GPT 5.4 judge alone, after all 3 debater engines and Cursor itself missed them. The review cost a fraction of what Cursor's own model would charge for the same depth.
Read the full case studyModel switching: advisory only
Cursor does not expose a model-switch API. MegaLens can recommend switching to a cheaper model for specific steps, but you have to change the model dropdown manually.
MegaLens can suggest: "This step is mechanical. Consider switching to a cheaper model."
But Cursor's model selection is manual. The suggestion appears in the findings, not as an automated switch.
In Claude Code and Codex CLI, exec-shift directives are executable via command-line flags. Cursor doesn't have this capability yet.
Honest Limitations
- —
Cannot switch Cursor's active model automatically.
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All cost recommendations are text-based, not executable.
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Cursor does not expose a model-switch API.
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MegaLens runs as an MCP server but cannot control Cursor's behavior.
Pricing
Free
$0/mo
BYOK
- 5 reviews/day
- 30/month fair-use
Pro
$15/mo
Managed keys
- Unlimited reviews
Managed Credits
$9/M
Blended tokens
- Pay-as-you-go