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Prompt tips for marginal's generate_variants tool

Updated June 2026 · Hosted email MCP server

The generate_variants tool turns a short prompt into a batch of subject line candidates ready for a subject line A/B test. The quality of those candidates depends almost entirely on what you tell it. These marginal generate_variants prompt tips help you get sharper output from your AI agent on the first call.

Give the tool real context, not a topic

A prompt like "write subject lines for a sale" produces generic candidates. generate_variants does much better when you describe the audience, the offer, and the action you want recipients to take. The more concrete the brief, the more distinct and testable your variants become.

Treat the prompt as a creative brief you'd hand to a copywriter. Include constraints up front so the agent doesn't waste variants on tones or formats you'll never ship.

Ask for variety across hypotheses

A strong subject line A/B test compares ideas, not synonyms. If every variant says the same thing five ways, the test teaches you nothing. Prompt generate_variants to span different angles so the lift metrics you get from get_results actually point to a winning strategy.

When you frame each variant around a distinct hypothesis, the within-test lift data becomes a map of what your audience responds to, not just which wording edged out the others.

Wire it into your workflow

Because marginal is a hosted email MCP server, generate_variants is one call in a sequence your AI agent can run end to end. Once you're happy with the candidates, pass them to launch_test, then pull open and click tracking with get_results, and let recommend_next suggest the follow-up.

Connect from any MCP client — Cursor, Claude Desktop, Claude Code, Windsurf, Cline, Continue, Zed, or OpenAI Codex — point it at https://marginal.sh/mcp, and authenticate with your Bearer API key. The free tier covers 100 experiments per month for AI agent email marketing experiments.

Common prompt mistakes to avoid

Most weak generate_variants output traces back to an underspecified prompt or an overloaded one. Keep your instructions concrete but focused so the agent can actually act on them.

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