Tool overload kills AI performance. We offer two ways to avoid it.
Connect a specific bundle to ChatGPT or Claude. AI sees only 5-15 tools for that workflow—sales, dev, marketing. No confusion, fast execution.
When you need insights across multiple services, the Hub discovers tools on-demand and writes code to orchestrate them—without showing AI all 50+ tools upfront.
"Which deals have gone cold and have support issues?" The AI checks HubSpot deals, filters by activity date, cross-references with your ticketing system, and tells you exactly which 7 need attention.
"What topics should I write about?" AI pulls traffic data, identifies top performers, analyzes themes, checks keyword opportunities in Ahrefs, and gives you specific topic recommendations.
"Flag accounts with declining engagement." AI identifies top accounts by revenue, pulls engagement metrics, checks support history, computes health scores, and ranks by risk.
"Who haven't I contacted in 60 days?" AI pulls your Affinity pipeline, checks last contact dates, filters and sorts by check size, hands you a prioritized outreach list.
The Hub doesn't show AI 50 tools upfront. Instead, it uses meta-tools to discover what's available on-demand.
AI starts with just 3 meta-tools: list bundles, search tools, get tool info. It finds what it needs only when it needs it.
Once it knows what tools to use, AI writes Python code to call them—processing hundreds of records in a single execution.
The code runs behind the scenes. You get the answer—"7 deals need attention"—not 200 raw records dumped in chat.
This approach comes from Anthropic's research on advanced tool use and code execution with MCP. We took their patterns and made them work for MCPBundles.