Categories › Business & Finance › E-commerce & marketplaces
Measured share of answer for 20 SaaS brands. An open dataset, not an audit of your site.
Remote server: your requests and data go to dabyte.ai. Claude Code: claude mcp add --transport http dabyte-ai-visibility-index https://dabyte.ai/mcp · Claude app: Settings → Connectors → Add custom connector.
The tools this server listed when we connected to it, in its own words.
get_visibility_index — The whole current release in one call: every tracked brand in SaaS & AI tools with its rank, share of answer overall and per engine, commercget_brand_visibility — One brand's standing in the current DABYTE release: share of answer per engine, rank, quadrant, how many panel prompts name it, and which onlist_tracked_brands — The names and slugs of every brand in the DABYTE index — a lookup table, nothing else. No scores, no ranks. Use it for two things: to turn aget_history — Every DABYTE release ever published, as a series per brand: share of answer at each weekly measurement with the date and panel version it waget_methodology — The rules behind every figure this server returns: the exact prompt panel and its version, which engines were measured, how share of answer Not tested by KitScout yet. When our harness runs it, pros, cons and the tasks we tried appear here, with the date and evidence.