Persuasion Taxonomy

com.coppicav1.0.0更新于 Oct 4, 2026

Plan and check marketing copy against the nine questions every reader asks. Free, no API key.

已验证Streamable HTTP可网页运行Data & Analytics Business & Commerce

概览

AI 生成的概览

让助手围绕读者会问的九个问题来规划、诊断和改进营销文案，检索 766 种说服技巧，并规划或解读 A/B 测试。

功能: 该服务器为助手提供读者模型：每位读者都会默默提出的九个问题。相关工具可围绕这些问题规划营销文案，通过指出文案回答了或遗漏了哪些问题来诊断草稿，解释文案为何无法转化，并检查标题和主张。它还能检索从真实广告中整理的 766 种说服技巧目录，返回带示例的完整条目，并规划或解读 A/B 测试结果。它自身不调用任何 AI，思考由你的模型完成。
适用场景: 当助手需要撰写或审阅广告、落地页、邮件或销售信，而你希望在发布前先做检查时，值得添加。它也适合诊断有点击但无销售的文案、寻找不那么套路化的建立信任方式，以及确定或解读 A/B 测试。
运行要求: 通过 Streamable HTTP 连接托管地址的远程 MCP 端点；无需登录、API 密钥或环境变量。也可以用 npx 在本地运行。使用远程方式时需要能访问该托管端点。

安装前请注意

服务器声明不保留你发送的内容：文案、简报和测试数据仅用于当次请求随后丢弃，托管方会像普通网站一样短期保留请求日志。它不写入数据，也不涉及付款。使用托管端点时，你提交的文案和测试数据仍会离开本机。

安装

在 SourceWeft 中

打开控制台中的 Persuasion Taxonomy，将其添加到工作区。
为需要使用其工具的对话启用该服务。

Web executable，通过 Streamable HTTP。远程服务在工作区中配置后即可从网页运行时运行。

其他 MCP 客户端

把它添加到你客户端的 mcpServers 配置中。

{
  "mcpServers": {
    "persuasion-taxonomy": {
      "type": "http",
      "url": "https://taxonomy.coppica.com/mcp"
    }
  }
}

README

Persuasion Taxonomy MCP

Most AI-written marketing copy fails for the same reason. The model answers every question the reader has with the most expected move, and the most expected move is exactly what readers have learned to skim past.

This server gives your AI a better model of the reader. Everyone who reads an ad, a page or an email is silently asking nine questions:

Why am I even reading this?
Is this written for me?
How bad is my problem, really?
Am I thinking about this correctly?
Can I trust the claim?
What does my life look like after?
What's stopping me from saying yes?
Why should I act right now?
Do I like and trust who's speaking?

Copy persuades when it answers the questions its goal needs, answers them truthfully, and the answers fit together. The server helps your AI plan copy around those questions, check a draft against them before anyone sees it, and reach past the obvious move for one of 766 persuasion techniques documented from real advertising. It also does the math behind A/B tests.

It's free and needs no API key. It makes no AI calls of its own, either: your model does the thinking, and the server only serves the catalog and runs plain code.

Connect it

The hosted server lives at https://taxonomy.coppica.com/mcp. Any client that accepts a remote MCP server (Streamable HTTP) can use that URL as it is.

Claude Code

Claude Code plugin. This adds the server along with a short skill that tells Claude when to reach for it.

/plugin marketplace add Otha-Labs/persuasion-mcp/plugin install persuasion-taxonomy@persuasion-taxonomy

/plugin marketplace add Otha-Labs/persuasion-mcp/plugin install persuasion-taxonomy@persuasion-taxonomy

Claude, ChatGPT and other apps with custom connectors. Add a custom connector and paste in the URL above. There's nothing to sign in to.

Cursor (.cursor/mcp.json)

VS Code (.vscode/mcp.json)

json

{ "servers": { "persuasion-taxonomy": { "type": "http", "url": "https://taxonomy.coppica.com/mcp" } } }

{ "servers": { "persuasion-taxonomy": { "type": "http", "url": "https://taxonomy.coppica.com/mcp" } } }

Run it on your own machine (any client that launches local servers)

json

{ "mcpServers": { "persuasion-taxonomy": { "command": "npx", "args": ["-y", "@coppica/persuasion-mcp"] } } }

{ "mcpServers": { "persuasion-taxonomy": { "command": "npx", "args": ["-y", "@coppica/persuasion-mcp"] } } }

What your AI can do with it

You don't need to name the tools. Ask for what you want and a capable model reaches for the right one, and in testing it did so in all 16 realistic requests we tried, without being told the server existed.

When someone asks...	The tool that answers
"Write me a landing page / ad / email / sales letter"	`plan_marketing_copy` plans it around the nine questions before a word is written
"Is this good?", "Roast this", "What's wrong with this copy?"	`diagnose_marketing_copy` shows which questions the copy answers, which it skips, and what reads as machine-written
"My ad gets clicks but no sales"	`explain_why_not_converting` works back from the symptom to the question going unanswered
"Give me other ways to build trust", "Make this less generic"	`find_persuasion_techniques` searches the catalog for a less expected move
"What is this technique called?", "How does it work?"	`get_persuasion_technique` returns the full entry, with real examples and when it backfires
"Which of these headlines is best?"	`check_headlines` says what each line is betting on and which two to test
"Is this claim believable?", "Is this button any good?"	`check_marketing_claims` catches vague superlatives, unproven promises and stock buttons
"How long should I run this test?"	`plan_ab_test` sizes the test and frames it so the result teaches you something
"Did my test win?"	`read_ab_test_result` reads the result and warns you when it might be lying

It also offers four ready-made prompts (/roast, /brief, /why-not-converting and /next-test) and the nine questions as a resource you can read in full.

What it keeps

Nothing you send it. Your copy, your brief and your test numbers are used to answer that one request and then dropped. The hosted server logs which tool was called and when, and like any website, its host keeps ordinary request logs for a short time. The full policy is at taxonomy.coppica.com/privacy.

Where the techniques come from

Every technique is an entry in The Persuasion Taxonomy, a catalog of persuasion techniques documented from real ads, sales letters, emails and pages, with examples from the 1920s to now. The catalog is free under CC BY 4.0. When your AI uses a technique, it names the technique and links to its page, so your reader can see the real examples behind the advice.

Develop

bash

npm installnpm run buildnpm run smoke            # connects as a client over stdio and calls every toolnpm run smoke -- --http  # the same, over Streamable HTTPnpm run voice            # checks every model-facing string for machine tells

npm installnpm run buildnpm run smoke            # connects as a client over stdio and calls every toolnpm run smoke -- --http  # the same, over Streamable HTTPnpm run voice            # checks every model-facing string for machine tells

The catalog in data/ is a snapshot. npm run export-data refreshes it, and it needs read access to Coppica's database, so outside contributors can work against the snapshot as it is.

The src/http.ts module exports handleMcpRequest(request), which takes a web-standard Request and returns a Response. That makes it easy to host anywhere that speaks fetch: a Next.js route, a Vercel or Cloudflare function, or plain Node 18 and up.

Voice

Every word the server shows a model reads the way a good direct-response writer would write it: plain words, full sentences that lead into each other, no em dashes, no shouted labels and no "it's not X, it's Y". That goes for the instructions, the tool descriptions, every line of output and every error message. A model picks up the voice of whatever it reads and passes it on to whoever it's writing for, so the tool has to model the writing it asks for. src/voice.ts turns the scorers' findings into plain sentences, and npm run voice keeps it honest.

License

The code is MIT. The catalog data in data/ is CC BY 4.0 and belongs to The Persuasion Taxonomy by Coppica.

About

Built by Coppica. This server knows how readers think. Coppica connects that same model of the reader to your live conversion data, so over time it learns which answers actually sell to your buyers.

来源：README.md，提交 a1dd265

工具

工具元数据尚未被收录。

版本历史

v1.0.0最新Oct 4, 2026