Enables AI agents to access a codebase context, select relevant files, and route queries to the appropriate AI model based on complexity, all through an MCP interface.
Exposes a developer's local context (communication preferences, stack, repos, memory models) to AI agents via MCP tools and resources, enabling them to bootstrap with local guidelines and reduce context hallucination.
Provides AI agents with operational customer context, including typed revenue objects, persistent state, scoped tools, and human-in-the-loop handoffs through MCP, REST, and CLI.
Serves reusable SDLC agent roles and review checklists over MCP, enabling AI coding agents to execute structured product analysis, solution architecture, code review, and release management tasks in GitHub-first projects.