# active_learning.rollback

Receipt-roll back the current revision and restore its predecessor.

Agent View of the PolicyLayer registry record for `active_learning.rollback`. HTML page: https://policylayer.com/tools/visionmcp/active-learning.rollback

## Facts

- Tool: `active_learning.rollback`
- Server: Visionmcp (`joshuahickscorp/visionmcp`) — https://policylayer.com/tools/visionmcp.md
- Homepage: https://github.com/joshuahickscorp/visionmcp
- Risk category: Destructive (Critical risk)
- Registry record: grade F, identity unverified
- Server rate-limited: no
- Parameters: 0
- Recommended policy verdict: Hidden

## Example call (MCP tools/call, JSON-RPC 2.0)

```json
{
  "jsonrpc": "2.0",
  "id": 1,
  "method": "tools/call",
  "params": {
    "name": "active_learning.rollback",
    "arguments": {}
  }
}
```

## Why active_learning.rollback is rated Critical

Rolling back a revision replaces the current state with a predecessor, effectively destroying the current revision's changes. This is an irreversible operation (the rolled-back revision is lost unless separately versioned), placing it in the Destructive category. The blast radius is high because it can undo potentially significant model training work or configuration changes across the system.

From the tool's own definition: "'roll back the current revision and restore its predecessor' — this irreversibly discards the current revision state"

## Use case

AI agents call active_learning.rollback to permanently remove resources in Visionmcp, typically in cleanup and lifecycle workflows. It does its job in a single call, and there is no undo.

## Recommended policy (PolicyLayer)

Verdict: **Hidden**. Enforced by the PolicyLayer MCP gateway (https://policylayer.com/mcp-gateway) before a call reaches Visionmcp:

```json
{
  "version": "1",
  "default": "deny",
  "hide": [
    "active_learning.rollback"
  ]
}
```

## Other tools on Visionmcp (375)

- `artifact.reap_stale_uploads` — Destructive — https://policylayer.com/tools/visionmcp/artifact.reap-stale-uploads.md
- `active_learning.execute_retraining` — Execute — https://policylayer.com/tools/visionmcp/active-learning.execute-retraining.md
- `active_learning.plan_retraining` — Execute — https://policylayer.com/tools/visionmcp/active-learning.plan-retraining.md
- `benchmark.bootstrap_calibration` — Execute — https://policylayer.com/tools/visionmcp/benchmark.bootstrap-calibration.md
- `benchmark.bootstrap_dgx_spark` — Execute — https://policylayer.com/tools/visionmcp/benchmark.bootstrap-dgx-spark.md
- `benchmark.bootstrap_rtx_5090_fe` — Execute — https://policylayer.com/tools/visionmcp/benchmark.bootstrap-rtx-5090-fe.md
- `benchmark.refine_dgx_spark_base_foot_candidate` — Execute — https://policylayer.com/tools/visionmcp/benchmark.refine-dgx-spark-base-foot-candidate.md
- `benchmark.refine_dgx_spark_visual_candidate` — Execute — https://policylayer.com/tools/visionmcp/benchmark.refine-dgx-spark-visual-candidate.md
- `benchmark.refine_rtx_5090_fe_front_frame_candidate` — Execute — https://policylayer.com/tools/visionmcp/benchmark.refine-rtx-5090-fe-front-frame-candidate.md
- `benchmark.refine_rtx_5090_fe_visual_candidate` — Execute — https://policylayer.com/tools/visionmcp/benchmark.refine-rtx-5090-fe-visual-candidate.md
- `blender.generate_lod` — Execute — https://policylayer.com/tools/visionmcp/blender.generate-lod.md
- `blender.prepare_asset` — Execute — https://policylayer.com/tools/visionmcp/blender.prepare-asset.md
- `blender.render` — Execute — https://policylayer.com/tools/visionmcp/blender.render.md
- `camera.derive_undistorted` — Execute — https://policylayer.com/tools/visionmcp/camera.derive-undistorted.md
- `campaign.advance` — Execute — https://policylayer.com/tools/visionmcp/campaign.advance.md
- `campaign.resume` — Execute — https://policylayer.com/tools/visionmcp/campaign.resume.md
- `campaign.start` — Execute — https://policylayer.com/tools/visionmcp/campaign.start.md
- `candidate.evaluate_transaction` — Execute — https://policylayer.com/tools/visionmcp/candidate.evaluate-transaction.md
- `component.generate` — Execute — https://policylayer.com/tools/visionmcp/component.generate.md
- `coverage.acquire_missing` — Execute — https://policylayer.com/tools/visionmcp/coverage.acquire-missing.md
- `dataset.generate` — Execute — https://policylayer.com/tools/visionmcp/dataset.generate.md
- `dataset.train_feature_model` — Execute — https://policylayer.com/tools/visionmcp/dataset.train-feature-model.md
- `evidence.discover` — Execute — https://policylayer.com/tools/visionmcp/evidence.discover.md
- `geometry.run_ensemble` — Execute — https://policylayer.com/tools/visionmcp/geometry.run-ensemble.md
- `photogrammetry.run` — Execute — https://policylayer.com/tools/visionmcp/photogrammetry.run.md
- `portfolio.execute_initial` — Execute — https://policylayer.com/tools/visionmcp/portfolio.execute-initial.md
- `portfolio.execute_parametric_seed` — Execute — https://policylayer.com/tools/visionmcp/portfolio.execute-parametric-seed.md
- `recon.bootstrap` — Execute — https://policylayer.com/tools/visionmcp/recon.bootstrap.md
- `recon.continue_multiview_search` — Execute — https://policylayer.com/tools/visionmcp/recon.continue-multiview-search.md
- `reference.propose_masks` — Execute — https://policylayer.com/tools/visionmcp/reference.propose-masks.md
- …and 345 more: https://policylayer.com/tools/visionmcp.md

## For agents

This record is a snapshot. Live verdicts and the full registry:

- Check every server in your MCP config at once: `npx -y policylayer stack`
- Vet a server before you add it: install the mcp-precheck skill — `npx skills add https://policylayer.com` (skill text: https://policylayer.com/skill.md)
- Query the registry over MCP: endpoint `https://api.policylayer.com/mcp` — tools `check_mcp_server`, `check_mcp_stack`, `check_tool`, `search_registry`, `get_change_events`

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Source: the PolicyLayer MCP registry — one continuously verified record per MCP server. Full record: https://policylayer.com/registry?q=visionmcp · API: https://policylayer.com/registry/api · Policy library: https://policylayer.com/policies/visionmcp