Add three new patrol tools that enable the LLM to create findings via
tool calls instead of relying on output parsing:
- patrol_report_finding: Create a structured finding with validation
- patrol_resolve_finding: Mark a finding as resolved
- patrol_get_findings: Query active findings for a resource
These tools are only functional during a patrol run when PatrolFindingCreator
is set on the executor. This approach is more reliable than parsing
JSON from LLM output.
- Merge tools_infrastructure.go, tools_intelligence.go, tools_patrol.go,
tools_profiles.go into their respective domain tools
- Expand tools_control.go with command execution logic
- Expand tools_discovery.go with resource discovery handlers
- Expand tools_storage.go with storage-related operations
- Expand tools_metrics.go with metrics functionality
- Update tests to match new structure
This consolidation reduces file count and groups related functionality together.
Major new AI capabilities for infrastructure monitoring:
Investigation System:
- Autonomous finding investigation with configurable autonomy levels
- Investigation orchestrator with rate limiting and guardrails
- Safety checks for read-only mode enforcement
- Chat-based investigation with approval workflows
Forecasting & Remediation:
- Trend forecasting for resource capacity planning
- Remediation engine for generating fix proposals
- Circuit breaker for AI operation protection
Unified Findings:
- Unified store bridging alerts and AI findings
- Correlation and root cause analysis
- Incident coordinator with metrics recording
New Frontend:
- AI Intelligence page with patrol controls
- Investigation drawer for finding details
- Unified findings panel with actions
Supporting Infrastructure:
- Learning store for user preference tracking
- Proxmox event ingestion and correlation
- Enhanced patrol with investigation triggers
- Updated AI providers and tests for context/tenant awareness
- Refactored tool executor for multi-tenant state handling
- Added new tests for Docker control and update tools
This refactoring removes the MCP (Model Context Protocol) server layer and
converts AI tools to be called directly by the chat service.
Key changes:
- Rename package from internal/ai/mcp to internal/ai/tools
- Remove server.go - tools no longer exposed via MCP server
- Tools are now called directly by the chat service via ExecuteTool()
New tools added:
- Kubernetes: clusters, nodes, pods, deployments (4 tools)
- PMG: mail gateway status, mail stats, queues, spam stats (4 tools)
- Infrastructure: snapshots, PBS jobs, backup tasks, network stats,
disk I/O, cluster status, swarm, services, tasks, recent tasks,
physical disks, RAID status, host Ceph, resource disks (14 tools)
- Patrol: connection health, resolved alerts (2 tools)
Test coverage:
- Added comprehensive test files for adapters, infrastructure,
patrol, profiles, and query tools
Total tools: 50 (was ~25)
2026-01-19 19:17:24 +00:00
Renamed from internal/ai/mcp/executor.go (Browse further)
