@raimonds.simanovskis Great to hear you are interested!
Tool discovery: Rovo uses progressive disclosure. Only lightweight metadata (name + short description, ~50-100 tokens per tool) is loaded at discovery time. Full schemas are hydrated only when a tool is selected. So 7+ tools won’t pollute the context.
Many tools vs single tool: We recommend individual tools. A single tool with an action parameter hides capability from the discovery layer and forces two-step reasoning. Admin controls (enable/disable) will also operate at the individual tool level.
On manifest versioning: Tool definitions (name, description, schema) are part of the manifest today, so changes require a new version. However, the upcoming rovo:skill module addresses the dynamic instructions problem. Skill content is deployed as a resource file, not in the manifest, so you can update the “when and how to use eazyBI” guidance on each forge deploy without publishing a new app version. We have a separate RFC for skills here: RFC-137: Forge Module for AI Skills
Recommended approach: Define individual actions under rovo:mcp for precise tool selection, and pair with a rovo:skill for dynamically updatable instructions that teach when and how to use your tools together.