# recon.audit_existing

Queue an authoritative audit of the current imported or generated scene.

Agent View of the PolicyLayer registry record for `recon.audit_existing`. HTML page: https://policylayer.com/tools/visionmcp/recon.audit-existing

## Facts

- Tool: `recon.audit_existing`
- Server: Visionmcp (`joshuahickscorp/visionmcp`) — https://policylayer.com/tools/visionmcp.md
- Homepage: https://github.com/joshuahickscorp/visionmcp
- Risk category: Read (Low risk)
- Registry record: grade F, identity unverified
- Server rate-limited: no
- Parameters: 0
- Recommended policy verdict: Allowed

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

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

## Why recon.audit_existing is rated Low

An audit typically reads and analyzes existing data without modifying it. However, the description mentions 'Queue' which implies triggering an external operation, and 'authoritative' suggests it may write results somewhere. The description is sparse, so confidence is moderate.

From the tool's own definition: "'audit of the current imported or generated scene' — this is an inspection/verification action on existing data"

## Use case

AI agents call recon.audit_existing to retrieve information from Visionmcp without modifying anything. It is typically the context-gathering step in research, monitoring, and reporting workflows, before the agent takes action elsewhere.

## Recommended policy (PolicyLayer)

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

```json
{
  "version": "1",
  "default": "deny",
  "tools": {
    "recon.audit_existing": {}
  }
}
```

## Other tools on Visionmcp (375)

- `active_learning.rollback` — Destructive — https://policylayer.com/tools/visionmcp/active-learning.rollback.md
- `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
- …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`

---

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