recomention

io.github.recomention由 GitHub 帳號 recomention 發布v0.1.3更新於 Nov 3, 2026

檢查AI引擎是否推薦某款產品：公開的AI排名和推薦報告。

AI 翻譯由 AI 模型根據下方的原文描述翻譯，可能有誤。

原文描述

Check whether AI engines recommend a product: public AI rankings and recomention reports.

已驗證Streamable HTTP可網頁執行資料與分析商業與電商AI 與機器學習

概覽

recomention 的託管 MCP 服務（mcp.recomention.com）；需要 OAuth 登入；Streamable HTTP；8 個工具。

暫無 AI 概覽：以下資訊來自註冊中心條目和服務本身。

端點
mcp.recomention.com
最近檢查

安裝

在 SourceWeft 中

  1. 開啟 儀表板中的 recomention，將其新增到工作區。
  2. 連接 所需的 憑證。它們會被私密儲存，絕不會在公開頁面上展示。
  3. 為需要使用其工具的對話啟用該服務。

Web executable，透過 Streamable HTTP。 遠端服務在工作區中設定後即可從網頁執行環境執行。

交給代理程式安裝

把這段提示詞貼上到 Claude Code、Codex、Cursor 等代理程式，或 SourceWeft 對話中。代理程式會讀取該伺服器的安裝指南，先向你展示發布者、端點和登入方式，經你同意後再把它新增到你的客戶端。金鑰不會寫進任何命令。

閱讀 https://sourceweft.com/mcp/io.github.recomention%2Fmcp/install.md，並按其中說明把這個 MCP 伺服器接入我的客戶端。修改任何設定之前，先告訴我它的發布者、端點和登入方式，等我確認。不要把 API 金鑰寫進命令或我會提交的檔案；問我金鑰應從哪裡取得。

檢視代理程式所依據的安裝指南

其他 MCP 客戶端

把它新增到你客戶端的 mcpServers 設定中。 客戶端會在首次連接時完成 OAuth 登入。

{
  "mcpServers": {
    "recomention": {
      "type": "http",
      "url": "https://mcp.recomention.com/mcp"
    }
  }
}

README

@recomention/mcp

An MCP server for recomention: ask Claude Code, Cursor or Codex "is my product recommended by ChatGPT for meeting scheduling?" and have it start a report, wait for it, and read the result — every engine's "named in n of m answers", who is recommended instead, the sources the engines read, and the fix list.

It is a thin stdio client for the public JSON API v1 at https://api.recomention.com/v1. It holds no state. It runs locally, as a process your MCP client starts. For ChatGPT, Claude.ai and other clients that connect to a remote server, use the hosted connector at https://mcp.recomention.com/mcp instead: it signs in with your recomention account (OAuth), no API key needed. Setup for each client: https://docs.recomention.com/mcp/.

Requires Node.js 20 or newer (npx comes with it).

Environment

Variable
RECOMENTION_API_KEYrequired for reportsCreate one at /account → Developers. Shown once. Without it, public rankings and suggested questions still work.
RECOMENTION_BASE_URLoptionalDefaults to https://api.recomention.com. An older https://www.recomention.com (or the apex, a web preview, or a local Next dev server on port 3000–3099) still works: it reaches the site's deprecated /api/v1 alias. Any other bare origin, such as the API preview Worker or a local API Worker (http://localhost:8790), uses /v1.
OPENAI_API_KEY, ANTHROPIC_API_KEY, PERPLEXITY_API_KEY, GEMINI_API_KEY, XAI_API_KEY, DATAFORSEO_API_KEY (Google AI Overview, login:password)optionalOnly for funding: "byok" (Bring Your Own Key), which needs an active Agent plan on the account. Any subset works and engines without a key are reported as skipped, but one of OpenAI, Anthropic or Gemini is required. Read from this server's environment, never from a tool argument, so your keys never pass through the model's context.

Install

Claude Code

bash
claude mcp add recomention \  --env RECOMENTION_API_KEY=YOUR_API_KEY \  --env RECOMENTION_BASE_URL=https://api.recomention.com \  -- npx -y @recomention/mcp

Replace YOUR_API_KEY with your key. Add --scope user to use it in every project. Check it with claude mcp list, or /mcp inside a session.

Cursor (~/.cursor/mcp.json, or .cursor/mcp.json in a project) and other JSON-configured clients

json
{  "mcpServers": {    "recomention": {      "command": "npx",      "args": ["-y", "@recomention/mcp"],      "env": { "RECOMENTION_API_KEY": "<key>", "RECOMENTION_BASE_URL": "https://api.recomention.com" }    }  }}

Keep a project-level .cursor/mcp.json that holds a key out of version control.

Codex

bash
codex mcp add recomention \  --env RECOMENTION_API_KEY=YOUR_API_KEY \  --env RECOMENTION_BASE_URL=https://api.recomention.com \  -- npx -y @recomention/mcp

or in ~/.codex/config.toml:

toml
[mcp_servers.recomention]command = "npx"args = ["-y", "@recomention/mcp"]env = { RECOMENTION_API_KEY = "<key>", RECOMENTION_BASE_URL = "https://api.recomention.com" }

Tools

ToolCost
recomention_rankings_listfreePublic AI rankings. No key needed.
recomention_rankings_get(slug, question?, engine?)freeOne ranking without answer texts: its questions numbered from 1 with each engine's usable answers, and every product named, as "named in n of m usable answers". Pass question (1-based) for that question's answers and citations, and engine with it for one engine's answers.
recomention_suggest_prompts({domain, category, description})freeBuyer questions from fixed templates. No model call. Never names the product.
recomention_report_start({domain, description, category, plan, prompts?, engines?, funding})spendsfunding: "credits" uses one agency credit (plan FULL); funding: "byok" runs on your provider keys and needs an active Agent plan; a refusal names the plan limit it hit.
recomention_report_status(slug)freePENDING / RUNNING / COMPLETE / FAILED, with progress.
recomention_report_get(slug, question?)freeThe report without answer texts, with per-question counts numbered from 1. Pass question (1-based: 1 is the first) for that question's answers and citations. 0 is deprecated and also means the first.
recomention_distribution_kit(slug)freeMarkdown files built from a finished report: product, competitors, AI visibility, where to get mentioned, channel plan.
recomention_reports_listfreeYour reports, newest first.

Reading results honestly: an engine in enginesSkipped did not run (for example "provider rejected the key") — it is never an engine that left the product out. A report with simulated: true is test data.

Setup prompt

The account page's Copy setup prompt button copies this (with the site's own URL). Paste it into your agent:

text
Set up the recomention MCP server so you can check whether AI engines (ChatGPT, Claude, Perplexity, Gemini, Grok) recommend a product.
1. Add this MCP server to your MCP configuration (Claude Code: `claude mcp add`; Cursor: ~/.cursor/mcp.json; Codex: ~/.codex/config.toml):   command: npx -y @recomention/mcp   env: RECOMENTION_API_KEY=<ask me for my API key; never write it into a repository>        RECOMENTION_BASE_URL=https://api.recomention.com   Claude Code one-liner: claude mcp add recomention --env RECOMENTION_API_KEY=<key> --env RECOMENTION_BASE_URL=https://api.recomention.com -- npx -y @recomention/mcp2. Restart or reload MCP servers, then call recomention_rankings_list to confirm it works.
How to use it:- To check a product, first call recomention_suggest_prompts with its domain, category and a one-line description, show me the questions, and let me edit them.- Starting a report spends money: an agency credit, or calls on my own provider keys (that needs my Agent plan). Always ask me before calling recomention_report_start, and say which funding it will use.- On my own keys, any subset works, but one of OpenAI, Anthropic or Gemini is required. If the start is refused with a `limit` (plan, projects, reportsPerMonth, perDay, concurrent), tell me which limit and do not retry around it.- Reports take several minutes. Poll recomention_report_status about once a minute; do not start a second report for the same product while one is running.- When it is COMPLETE, call recomention_report_get and summarise: the overall score, each engine's "named in n of m answers", who is recommended instead, and the top three fixes. Quote counts as "n of m", never as yes/no.- An engine listed under enginesSkipped did not run (for example "provider rejected the key"). Say so; never describe it as an engine that did not mention the product.- A report with simulated: true is a test run with made-up answers. Say that plainly and do not draw conclusions from it.
Docs: https://www.recomention.com/developers

The canonical text lives in packages/core/src/config/developers.ts (setupPrompt); keep this copy in step with it.

Skills

skills/ holds agent skills in the slash-command style: copy a folder into .claude/skills/ (Claude Code) or your agent's equivalent.

  • ai-visibility-check — check one product end to end.
  • fix-list-review — turn a finished report into a prioritised, honest plan of work.
  • competitor-watch — compare who the engines name in a category, from public rankings and your reports.
  • distribution-kit — read a finished report's kit and work through its channel plan, one channel at a time.

Develop

bash
npm installnpm test            # in-memory client against the server, fake API; metadata/version checksnpm run smoke       # spawns the stdio server against RECOMENTION_BASE_URLnpm run build       # clean dist/ (bin: recomention-mcp)npm run smoke:pack  # packs the tarball (with pnpm), checks its contents, runs it against a fake local API

In the monorepo, run them as pnpm --filter @recomention/mcp <script>.

Run smoke --start only against a local server with RECOMENTION_MOCK=1: against a live deployment it spends a credit.

prepack empties and rebuilds dist/, so a pack or publish always ships the current sources and no test files. version lives in package.json; the server reads it at startup, and server.json (the MCP Registry entry, io.github.recomention/mcp) must carry the same number — a test fails otherwise.

This repository is published from the recomention monorepo, where development happens. Bugs and questions: issues or [email protected].

Licence

MIT.

來源：README.md，提交 b765396

工具

8

recomention_rankings_list

List AI rankings

List recomention's public AI rankings: for each category, which products the AI engines name when asked its buyer questions, month by month. Free.

無需輸入參數。

recomention_rankings_get

Get an AI ranking

One public AI ranking by slug: its questions with each engine's usable answers, and the most-named products as "named in n of m usable answers", overall and by engine. Answer texts are left out to fit your context: pass question (1-based: 1 is the first question, the number each summary entry shows) to read that question's answers, the products they named and their citations, and engine with it for one engine's answers only. Failed and not-run calls are counted apart, never as answers that named nobody. Free.

輸入參數slug必填enginequestion

recomention_suggest_prompts

Suggest buyer questions

Suggest buyer-intent questions to check for a product, from fixed templates. No model call and no cost. The product's own name never appears in a question. Show them to the user to edit before starting a report.

輸入參數countdomain必填category必填description

recomention_report_start

Start a report (spends a credit or your provider keys)

Start a recomention report: the questions are asked of every engine on the plan several times each, with live web search. THIS SPENDS MONEY: funding "credits" uses one agency credit from the user's account (plan must be FULL); funding "byok" runs on the user's own provider keys from this server's environment (OPENAI_API_KEY, ANTHROPIC_API_KEY, PERPLEXITY_API_KEY, GEMINI_API_KEY, XAI_API_KEY, DATAFORSEO_API_KEY as login:password for Google AI Overview) and needs an active Agent plan on the account. Any subset of keys works and engines without a key are reported as skipped, but one of OpenAI, Anthropic or Gemini is required. A refusal names the plan limit it hit. Always ask the user before calling it. Reports take several minutes: poll recomention_report_status about once a minute.

輸入參數planbranddomain必填enginesfunding必填promptscategory必填description必填idempotencyKey

recomention_report_status

Report status

Where a report is: PENDING, RUNNING, COMPLETE or FAILED, with answers stored so far. Free.

輸入參數slug必填

recomention_report_get

Get a report

A report as JSON: the overall score, each engine's runs mentioned of runs total, competitors named instead, cited sources, the fix list and, per question, how many usable answers named the product. Answer texts are left out to fit your context: pass question (1-based: 1 is the first question, the number each summary entry shows) to read that question's answers and citations. Engines in enginesSkipped did not run; never describe them as not mentioning the product. simulated: true means test data. Free.

輸入參數slug必填question

recomention_distribution_kit

Get the distribution kit

Markdown files built from a finished report, for planning distribution: product.md, competitors.md, ai-visibility.md, where-to-get-mentioned.md (third-party pages, subreddits and videos the engines cite for a rival and not for this product) and channel-plan.md (channels ranked by those citations, each with a first task). Read README.md first. Every count is "n of m answers"; nothing in it promises traffic. Show the user every draft before anything is posted. Free.

輸入參數slug必填

recomention_reports_list

List my reports

The reports in the account that owns the API key, newest first. Free.

無需輸入參數。

版本歷史

4
  1. v0.1.3最新Nov 3, 2026
  2. v0.1.2Nov 1, 2026
  3. v0.1.1Oct 31, 2026
  4. v0.1.0Oct 30, 2026

評分

已滿足適用檢查項的 100%

該評分衡量此收錄條目的完整程度，依據註冊中心條目、端點與公開來源計算得出。它不是對伺服器本身的評價，也不表示伺服器安全或目前可用：可連通性是帶日期的檢查結果，評分也不包含安裝量或使用者評價。

  • 近 30 天內可連通必需25 分
  • 已宣告認證方式必需10 分
  • 已列出工具15 分
  • 描述必需15 分
  • 圖示10 分
  • 文件15 分
  • 授權條款10 分