optifeed-radar

# Optifeed Radar [](https://www.npmjs.com/package/optifeed-radar) [](https://github.com/optifeed/optifeed-radar/actions/workflows/ci.yml) [](LICENSE) [](https://nodejs.org) **Open-source AI visibility checker. Now on npm - run it with `npx optifeed-radar`.** Is your brand recommended when buyers ask AI? Optifeed Radar checks whether ChatGPT, Perplexity, Gemini and Claude actually recommend you, and tells you where you stand against competitors. It runs locally, uses your own API keys, and has no Optifeed-hosted backend. It is built for two kinds of AI agents at once: it measures how **AI agents** see and recommend you, and it can be **run by your own AI agents** (CLI, JSON, and an MCP server). People also call this AI visibility, generative engine optimization (GEO), answer engine optimization (AEO), or AI-SEO. <p align="center"> <img src="https://raw.githubusercontent.com/optifeed/optifeed-radar/main/docs/assets/optifeed-radar-overview.png" alt="Optifeed Radar AI visibility dashboard and report" width="900"> </p> ## 60-second setup No install needed - `npx` fetches and runs it. The zero-key `audit` runs end to end with no API keys and no AI calls: ```bash npx optifeed-radar audit yourbrand.com ``` It checks AI-crawler access (robots.txt), llms.txt, schema.org structured data, meta basics, and your sitemap, then prints a 0-100 AI-readiness score. The `check` pipeline runs once you set at least one engine API key. Put it in a `.env` file in the directory you run from, or export it: ```bash echo "OPENAI_API_KEY=sk-..." > .env # any one engine key gets you started npx optifeed-radar check yourbrand.com ``` The CLI loads `.env` from the directory you run it in, so there is no shell setup step. Exporting the keys works too (`export OPENAI_API_KEY=...`), and an exported key always wins over the same key in `.env`. `config` shows which keys were found and which file they came from, never the values. It discovers your brand, generates a buyer-prompt pack, asks the engines, and scores recommendation, position, and share of voice into one AI Visibility Score. The score reads only the unbranded buyer questions (did the AI surface you unprompted); questions that name your brand are reported separately as reputation. All four engines are verified live against their production APIs (2026-07-20). Working from a clone instead? Run `npx tsx src/cli/index.ts <command>` so flags reach the CLI unchanged, or use the `npm run dev` script with `--` before the arguments (`npm run dev -- check yourbrand.com --report out.html`). ### See it in action Run a full visibility check from the terminal, from brand discovery and buyer prompt generation through live engine queries and scoring. <p align="center"> <a href="https://github.com/optifeed/optifeed-radar/blob/main/docs/assets/optifeed-radar-cli.mp4"> <img src="https://raw.githubusercontent.com/optifeed/optifeed-radar/main/docs/assets/optifeed-radar-cli-preview.png" alt="Watch the Optifeed Radar CLI demo" width="760"> </a> </p> <p align="center"><a href="https://github.com/optifeed/optifeed-radar/blob/main/docs/assets/optifeed-radar-cli.mp4"><strong>Watch the 15-second CLI demo</strong></a></p> ## What it does Optifeed Radar asks real AI engines real buyer questions and measures whether your brand gets recommended - not whether you rank in a search index, but whether the answer an AI gives a buyer names you. Grounded engines (which cite web sources) are reported separately from parametric ones (which answer from model weights alone), because they behave differently. An engine counts as grounded only for the answers where it actually searched: asking for grounded mode is a request a model can decline, so the report says when an engine searched on only some of its answers. [METHODOLOGY.md](METHODOLOGY.md) has the formula. The questions match what you sell. If you make your own products, buyers are asked what to buy and you are measured against rival makers. If you are a shop selling other companies' products, buyers are asked where to buy and you are measured against rival shops - product questions get answered with manufacturers, so scoring a shop on them reports a zero that says nothing about the shop. The tool works this out from your site and stores it as `businessType` in `profile.json`; edit it if it guessed wrong. One level down, `shopping` does the same thing for individual products you name (beta). Each product gets its own 0-100 visibility score, and the report is ordered by what the engines did: any product they answered about but never recommended leads, since that is the finding worth reading, then the rest by visibility, and last anything the run could not measure at all. The order you list your products in carries no ranking meaning; it only breaks ties between identical scores. Each product is checked twice over - category buying questions that never name it, and questions that do - and when a product is absent the report leads with the rival products the engines named instead, which is the more useful half of a zero. Because every product is asked its own questions, the scores say how decisively each one wins its own shelf, not that one product beats another. You name the products; nothing is imported or crawled. ## Use it from your AI agents (MCP) The `optifeed-mcp` server exposes the same capability to AI agents. It runs over stdio, and `npx` fetches it on demand - no clone or build needed. <p align="center"> <img src="https://raw.githubusercontent.com/optifeed/optifeed-radar/main/docs/assets/optifeed-radar-claude-desktop.png" alt="Optifeed Radar running through Claude Desktop via MCP" width="760"> </p> Claude Desktop (`claude_desktop_config.json`). The fastest way to open it is Settings -> Developer -> Edit Config, which creates the file if it does not exist yet. On disk it lives at: - macOS: `~/Library/Application Support/Claude/claude_desktop_config.json` - Windows: `%APPDATA%\Claude\claude_desktop_config.json` ```json { "mcpServers": { "optifeed-radar": { "command": "npx", "args": ["-y", "--package=optifeed-radar", "optifeed-mcp"], "env": { "OPENAI_API_KEY": "sk-..." } } } } ``` Claude Desktop reads that file at startup, so quit and reopen it after editing. Claude Code (`.mcp.json` in your project): ```json { "mcpServers": { "optifeed-radar": { "command": "npx", "args": ["-y", "--package=optifeed-radar", "optifeed-mcp"] } } } ``` Cursor (`.cursor/mcp.json`) and Windsurf (`mcp_config.json`) use the same shape: ```json { "mcpServers": { "optifeed-radar": { "command": "npx", "args": ["-y", "--package=optifeed-radar", "optifeed-mcp"] } } } ``` Working from a clone instead? Build first (`npm install && npm run build`), then run the server with `node` pointed at the built entrypoint - replace `/path/to/optifeed-radar` with your clone path: ```json { "mcpServers": { "optifeed-radar": { "command": "node", "args": ["/path/to/optifeed-radar/dist/mcp/index.js"] } } } ``` ### Example prompts Once it is connected, ask your AI agent in plain language. These map onto the five tools and the arguments they accept: - "Run a free AI-readiness audit on yourbrand.com." -> `audit_store`, no keys, no cost. - "What buyer questions should yourbrand.com be visible for?" -> `generate_buyer_queries`, so you can review the pack before paying for a run. - "Check yourbrand.com's AI visibility, quick mode, cap it at 20 cents." -> `check_visibility` with `quick` and `max_cost`. - "Check yourbrand.com on OpenAI and Perplexity only." -> `check_visibility` with `engines`. - "Check whether AI recommends my products: Aria 2, Presto X, Brew Mini, in that order, for yourbrand.com." -> `shopping_check`. Input order is not a ranking; it only breaks ties between products with identical scores. - "Check the Aria 2, a quiet home espresso machine, and the Presto X, a fast dual-boiler, on yourbrand.com. Cap it at one dollar." -> `shopping_check` with a descriptor per product and `max_cost`. Saying what each product IS is what rescues an opaque model name: without it the questions are guessed from the store category. - "What changed since the last visibility run on yourbrand.com?" -> `get_snapshot_diff`, free, and it needs two saved runs before it can compare. Start with the audit prompt: it needs no keys, so it confirms the server is wired up before anything spends API credit. `check_visibility` runs non-interactively (no confirmation prompt over MCP), so the `max_cost` cap is what bounds a run your AI agent starts - it defaults to $0.50. `shopping_check` is bigger, so its default cap scales with the list, at $0.20 per product you name. ## Tools and cost | Surface | Name | What it does | Cost | | ------- | ------------------------ | --------------------------------------------------------------- | ------------------------------ | | CLI | `audit` | Zero-key AI-readiness check (robots, llms.txt, schema, sitemap) | Free, no AI calls | | CLI | `check` | Full pipeline: buyer prompts, engines, AI Visibility Score | BYO keys | | CLI | `shopping` | Products you name: your ranking vs AI's, and the rival shelf | BYO keys | | CLI | `diff` | What changed between your last two runs | Free (reads a saved snapshot) | | CLI | `sources` | Domains the AI cited, and your share of voice | Free (reads a saved snapshot) | | CLI | `queries` | Show or export your buyer-prompt pack | Free | | CLI | `config` | Which engine keys are set, where state is stored | Free | | MCP | `check_visibility` | Run a visibility check for a domain | BYO keys | | MCP | `audit_store` | Run the zero-key readiness audit | Free | | MCP | `generate_buyer_queries` | Produce the buyer-prompt pack | BYO keys (usually under $0.05) | | MCP | `shopping_check` | Same product check, for your AI agents | BYO keys | | MCP | `get_snapshot_diff` | Compare two saved runs | Free | Cost transparency: `audit` queries no AI engines and costs nothing. `check` spends your own API credit. Measured on real runs (2026-07-20, `--quick` = 8 buyer prompts): | run | measured cost | | -------------------------------- | -------------- | | `audit` | free | | `check --quick`, one engine | about $0.09 | | `check --quick`, all four | $0.41 to $0.46 | | `check --quick --grounded`, four | $0.85 to $1.09 | Your cost varies with engine, prompt-pack size, and provider pricing. Grounded runs cost roughly 3x parametric ones, because web search is billed on top of tokens: Google charges per search query, and one answer can trigger several. The grounded range spans three real runs: two finished clean at about $0.85, and one that was reined in by its own cap spent $1.09, so treat the top of the range as the planning number. `shopping` was measured on 2026-07-23: two products across all four engines in `--grounded` mode, 32 answers, **$0.70 for the run - about $0.35 per product**. That is the expensive corner (grounded runs cost roughly 3x parametric ones), so a parametric four-engine run lands well under it. Each product costs about 4 prompts on every engine with a key, and products in the same category share their category questions, which are asked once and scored for each product. The MCP tool's default cap is $0.20 per product, which a grounded run will reach, so raise `max_cost` when you want grounded across four engines. Use `--max-cost` and start with two or three products. How long it takes, measured the same way (2026-07-22): | run | measured time | | -------------------------------- | ------------------ | | first `npx` (install, once) | about 8 seconds | | `audit` | 0.3 to 1.7 seconds | | `check --quick`, four engines | 47 to 51 seconds | | `check --quick --grounded`, four | about 97 seconds | The install figure was measured from the packed tarball with an empty npm cache, so a cold `npx optifeed-radar audit yourbrand.com` from the registry should finish in about ten seconds. A `check` takes as long as the engines take to answer: it queries several of them across a whole prompt pack, and that wait is provider latency we do not control. `check` reports live progress while it runs, so you can see which phase it is in rather than watching a blank terminal. Every run reports what it actually spent, split into setup (brand discovery and prompt generation) and engine calls, so you can reconcile it against your provider bill. Declining at the confirmation prompt still reports the setup cost, because discovery runs before that prompt. `--max-cost 0.20` caps spend. The cap is checked before every call and hitting it returns a partial result flagged as capped, never an error. It is a strong bound rather than an absolute ceiling: an engine's cost is not known until its call returns, so a run can exceed the cap by at most the cost of one unmeasured call per engine. Any overshoot is always reported, never hidden. Useful `check` flags: `--json` (raw envelope), `--report report.html` (self-contained report), `--max-cost 0.50`, `--quick` (smaller prompt pack), `--grounded` (web-search mode where engines support it), `--fail-under 50` (exit non-zero below a threshold, for CI), `--yes` (skip the cost prompt so an AI agent can run it unattended). ## Example ```bash npx optifeed-radar audit example.com # free readiness score npx optifeed-radar check example.com --quick --yes npx optifeed-radar shopping example.com --products "Aria 2, Presto X" --yes npx optifeed-radar diff example.com # what changed since last run npx optifeed-radar sources example.com # who the AI cited npx optifeed-radar config # which keys are set ``` `config` reports only whether each key is present, never the key value. For `shopping`, list up to 10 products per run in any order; only the first 10 are checked, and the report is ordered by what the engines did, not by what you typed. A file gives each product a descriptor, which is what rescues an opaque product name - "Aria 2" tells an engine nothing, "quiet home espresso machine" tells it everything: ```yaml products: - name: Aria 2 aliases: [Aria II] descriptor: quiet home espresso machine - name: Presto X descriptor: fast dual-boiler espresso machine ``` ```bash npx optifeed-radar shopping example.com --products-file products.yml --yes ``` ## FAQ **What is AI visibility?** Whether AI engines recommend your brand when a buyer asks them a question, rather than whether you rank in a traditional search index. It is also called generative engine optimization (GEO) or answer engine optimization (AEO). **Is there an MCP server?** Yes. The `optifeed-mcp` server exposes `check_visibility`, `audit_store`, `generate_buyer_queries`, `shopping_check` and `get_snapshot_diff` to your AI agents over stdio. **What does it cost?** The `audit` command is free and needs no keys. The `check` pipeline spends your own engine API credit: measured at about $0.09 for a quick single-engine run, $0.41 to $0.46 across all four, and $0.85 to $1.09 with `--grounded`. Every run reports what it spent, and `--max-cost` caps it. You bring your own keys; there is no Optifeed-hosted billing. **Where is my data stored?** Optifeed Radar runs locally, saves snapshots on your machine, and has no Optifeed-hosted backend. Each key is sent only to its corresponding AI provider as required to make API calls and is never logged; prompts and responses are handled under that provider's data policies. **Which engines does it support?** OpenAI (ChatGPT), Anthropic (Claude), Google (Gemini), and Perplexity. Set any one key to start; set more for broader coverage. **How is the score computed?** From sampling real engine answers to unbranded buyer questions, scoring recommendation, position, and share of voice. Scores are estimates and vary between runs. See [METHODOLOGY.md](METHODOLOGY.md) for the full method. ## For directory maintainers One-liners for awesome-mcp-servers, ClawHub, and Smithery listings: - **Optifeed Radar** - Ask real AI engines real buyer questions and score whether a brand gets recommended. CLI plus MCP server, runs locally, BYO keys. - **Optifeed Radar (MCP)** - `check_visibility`, `audit_store`, `generate_buyer_queries`, `shopping_check`, and `get_snapshot_diff` for measuring brand and product AI visibility (GEO / AEO) from your AI agents. Search intents this serves: AI visibility checker, does AI recommend my brand, generative engine optimization (GEO) tool, answer engine optimization (AEO), ChatGPT brand visibility, AI-SEO, MCP server for brand visibility. ## What this does NOT do - It is a point-in-time check, not continuous monitoring. - Scores are estimates from sampling and vary between runs - they are not a guaranteed ranking. - It does not find your products for you. `shopping` checks the products you name, up to 10 per run; there is no catalog or feed import. - `check` and `shopping` spend your own API credit; only `audit` is free. - It is not a traditional SEO rank tracker. Catalog discovery (pulling your products from a store or a feed) and feed linting against the Agentic Commerce Protocol (ACP) and the Universal Commerce Protocol (UCP) will arrive in later releases - join the waitlist at [optifeed.com](https://www.optifeed.com/). ## Status Under active development, and the repo is public so you can follow along. If this is useful to you, a star genuinely helps. Scores are estimates and say so. Your API keys are used only to call their corresponding providers and are never logged or stored by Optifeed Radar. ## License MIT More at optifeed.com: <https://www.optifeed.com/>