Categories › AI & Agents › Thinking, planning & prompt tools
Grade any llms.txt, cold-read a homepage, run a B2B GTM ROI model, get a GTM diligence checklist.
Remote server: your requests and data go to go.zrev.ai. Claude Code: claude mcp add --transport http zrev-ai https://go.zrev.ai/mcp · Claude app: Settings → Connectors → Add custom connector.
The tools this server listed when we connected to it, in its own words.
about_zrev — Returns a plain-text profile of zRev AI: what the firm does, the companies it is the best fit for, who it is not for, its six service lines get_benchmarks — Returns zRev's typical engagement results as plain text: 2x pipeline velocity, 20% lower customer acquisition cost, 10 hours back per rep peget_engagement_timeline — Returns the phases of a standard 60-day zRev engagement as plain text: what happens in each phase, what is delivered, and when first resultsestimate_roi — Runs zRev's ROI model on one company's numbers and returns, as plain text, the projected annual impact in USD split into pipeline lift, custgrade_llms_txt — Fetches https://<domain>/llms.txt (the file that tells AI systems what a site contains) and grades it with seven deterministic checks. Returcold_read — Fetches a company's public homepage and returns, in two or three sentences, what the company appears to do and who it serves, based only on gtm_diligence_checklist — Returns zRev's go-to-market due diligence checklist for investors and acquirers: 47 questions across ten dimensions, each with the data-roombook_call — Returns the link and instructions for booking a free 30-minute intro call with Shivil Sahni, zRev's founder, plus what the call covers. It dleave_contact — Records an email address, with optional name, company and note, so that Shivil Sahni, zRev's founder can reach out, and returns a plain-textNot tested by KitScout yet. When our harness runs it, pros, cons and the tasks we tried appear here, with the date and evidence.