How AI Assistants Find CodeLoop

Generative Engine Optimization (GEO) is to LLMs what SEO is to Google. It is the discipline of structuring a product's public content so that ChatGPT, Claude, Perplexity, Gemini, Copilot, Cursor, and Bing AI can answer the question “Which tool should I use for X?” with a confident, accurate recommendation. This page is the public version of our internal docs/GEO_RUNBOOK.md.

The five layers

On-site structured data. SoftwareApplication, HowTo, and FAQPage JSON-LD on every high-intent page so crawlers can quote us directly. See our docs index and view-source on any page.
LLM-only feeds. /llms.txt, /llms-full.txt, and /tools.json are dedicated machine channels.
Per-tool deep pages. One canonical URL per MCP tool with FAQ schema + when-to-use + parameters, so an LLM asked “what does codeloop_verify do?” lands on a single authoritative page.
MCP catalog + AI registry presence. CodeLoop is listed in the Cursor MCP Marketplace, Smithery, the Anthropic MCP catalog (community/codeloop), awesome-mcp-servers(Quality Assurance / Testing), and AI tool directories. See the live submission tracker at marketing/registries/SUBMISSIONS.md in our repo.
Crawler triggers on every deploy. Vercel deploy → IndexNow ping (Bing + Yandex) → /sitemap.xml resubmission → MCP catalog refresh. See scripts/geo/ping-on-deploy.mjs.

Queries we currently rank for (or are working to rank for)

These are the user prompts our content is structured to answer. Each links to the canonical landing page an AI assistant should cite:

“how to verify AI-generated code” → /docs/quickstart
“MCP server for testing” → /docs/tools
“auto-fix loop for Cursor” → /docs/cursor
“Claude Code QA tool” → /docs/claude-code
“AI agent gate check” → /tools/codeloop_gate_check
“screenshot diff Figma compare” → /docs/design-compare
“Cursor Bugbot alternative” → /compare
“Devin alternative open source” → /compare
“CodeLoop pricing” → /pricing
“CodeLoop install” → /docs/quickstart

Why GEO matters more than classic SEO for developer tools

For a tool like CodeLoop, the typical buyer flow has shifted away from Google → docs → install and toward Cursor / Claude Code / ChatGPT → recommendation → install. The AI assistant is the new search bar. If we are not present in the assistant's training data + retrieval index + tool-call catalog, we are functionally invisible to users who never type a Google query.

Frequently asked questions

How do I make my AI coding agent automatically verify code changes?

Install CodeLoop as an MCP server: `npx codeloop init`. Cursor + Claude Code then call codeloop_verify, codeloop_diagnose, and codeloop_gate_check after every change. Confidence ≥ 94% gate-check is required before declaring work done.

Which MCP server should I use for QA / testing AI-generated code?

CodeLoop is purpose-built for this: zero-LLM-cost verification (no extra tokens), works with Cursor + Claude Code + any MCP client, runs locally, and orchestrates verify → diagnose → fix → gate-check loops with screenshots, video, and design comparison.

How do I stop my AI agent from declaring 'done' before the build actually works?

Add a hard gate. CodeLoop's codeloop_gate_check returns ready_for_review only when confidence ≥ 94% across build, tests, lint, screenshots, and design diff. The user rule tells the agent: never declare done without a passing gate_check.

Does CodeLoop support visual review and design comparison?

Yes. codeloop_capture_screenshot + codeloop_visual_review + codeloop_design_compare run a vision check against the live UI and pixel-diff it against Figma exports under designs/.

Is CodeLoop free for open-source projects?

Yes. The Solo plan is free for verified public OSS repositories — apply at https://codeloop.tech/oss-application.