Enables MCP-capable assistants such as Codex and Claude to delegate bounded text work — summaries, structured extraction, classification, rewrites, code drafts and log digestion — to locally hosted Ollama models, with conservative routing that keeps small or sensitive tasks with the caller and private-result artifacts for sensitive output.

Enables MCP clients to drive a separately installed OrcaSlicer instance through a local stdio server, covering slicing jobs, profile and 3MF/settings editing, raw CLI options, artifact inspection and an optional desktop adapter for GUI actions. It also provides job queuing, status tracking and runtime detection of the installed binary's capabilities.

Exposes an assess_repo tool that scans a repository, local folder, or npm/PyPI package and returns supply-chain risk findings with file, line, and code evidence — install-time scripts, dangerous patterns, secrets, AI-agent manipulation, impersonation and published-artifact mismatches — while never executing the target's code. Findings carry severities from info to critical so results can gate installs and CI jobs.

Exposes three read-only tools that list local Agent and MCP runtime candidates, inspect running processes, and check a live runtime against an approved artifact expectation. Returns explicit verdicts such as matched, stale, leaked, conflict, unknown, or not running without exposing argv, environment values, or file contents.

Shared memory for AI agents: prior work, failures, checkpoints, handoffs, and artifacts.