Sensory-Grounding MCP

Give your AI coding agents real eyes and ears — and stop them from telling you "it's done" when it isn't.

A Model Context Protocol server that gives Claude Code, Codex, Antigravity, and any other MCP-compatible coding agent a closed-loop verification cycle for visual, audio, and video output — plus the enforcement hooks that make verification mandatory, not optional.

Built by Luclino Cruz for LuCross, a one-person, AI-agent-operated holding company, as part of the internal harness that governs every agent in its ecosystem. Released publicly because it solves a problem every agentic coding setup runs into, and there was no reason to keep it private.

The problem this solves

You ask an agent to fix a layout bug, re-render a thumbnail, or clean up a narration track. It edits the file, the tool call returns with no error, and the agent reports: "Done — looks great now."

Except it doesn't. The heading still overflows. The narration still clips. The video still has the wrong aspect ratio. The absence of an error is not proof of correctness — and an LLM has no way to see its own output unless you explicitly hand it eyes.

This is not a prompting problem. Telling a model "please actually check your work" in a system prompt does not work reliably — the research is unambiguous on this (Huang et al., ICLR 2024; Gou et al., CRITIC, ICLR 2024): self-correction without an external, deterministic signal degrades performance rather than improving it. What actually works is forcing a verifier into the loop — something outside the model's own judgment that the model cannot talk its way around.

Related MCP server: gossipcat

How it works

Three layers, each doing exactly one job:

┌──────────────────────────────────────────────────────────────┐
│  ENFORCEMENT — Claude Code hooks (outside the agent's control) │
│  PostToolUse (Write|Edit) → flags the file as pending review   │
│  Stop → blocks turn completion (exit code 2) until resolved    │
└───────────────────────────┬──────────────────────────────────┘
                             │
┌────────────────────────────▼──────────────────────────────────┐
│  CRITIC — the agent itself, in the same session                │
│  Looks at the screenshot/spectrogram/frames the sensor returns │
│  Assigns a 0-100 score against the task's success criteria     │
└────────────────────────────┬──────────────────────────────────┘
                             │
┌────────────────────────────▼──────────────────────────────────┐
│  SENSORS — 3 MCP tools, stateless, deterministic                │
│  visual_inspector · acoustic_inspector · video_inspector        │
└──────────────────────────────────────────────────────────────┘

The critical piece most "AI eyes" projects skip is the enforcement layer. A visual_inspector tool that the agent can call but doesn't have to call changes nothing — an agent under time pressure will skip it and report success anyway. This project wires the verification cycle into Claude Code's own hook system so the agent cannot end its turn with an unresolved, un-improved visual/audio/video change. No new orchestrator, no external critic model, no extra API cost beyond what you already pay for the agent itself.

The 6 tools

Forced Optimization

Borrowed from ReLook (Li et al., Tencent, ACL 2026): an edit is only accepted if score_new > score_previous for that exact file. A regression that "at least didn't make it much worse" is not accepted — the agent must revert or try a different approach. This turns iteration into a monotonically improving trajectory instead of a random walk.

Quick start

git clone https://github.com/luclinocruz/lucross-sensory-grounding-mcp.git
cd lucross-sensory-grounding-mcp
python -m venv .venv
.venv/Scripts/activate   # or: source .venv/bin/activate on Linux/macOS
pip install -r requirements.txt
playwright install chromium

There are two ways to install this — pick based on how broadly you want the gate to apply. Both use the exact same server and hook script; only the config differs.

Option A — Project-scoped (one repo, explicit root)

Best for: shared/team repos, or any project where you want the sandbox root spelled out with zero ambiguity.

.mcp.json in your project root:

{
  "mcpServers": {
    "sensory-grounding": {
      "command": "/absolute/path/to/lucross-sensory-grounding-mcp/.venv/bin/python",
      "args": ["/absolute/path/to/lucross-sensory-grounding-mcp/server.py"],
      "cwd": "/absolute/path/to/your/project",
      "env": { "SGS_SANDBOX_ROOT": "/absolute/path/to/your/project" }
    }
  }
}

.claude/settings.json in the same project:

{
  "hooks": {
    "PostToolUse": [{
      "matcher": "Write|Edit",
      "hooks": [{ "type": "command",
        "command": "/absolute/path/to/.venv/bin/python /absolute/path/to/enforce_inspect.py --stage post-edit --root /absolute/path/to/your/project" }]
    }],
    "Stop": [{
      "hooks": [{ "type": "command",
        "command": "/absolute/path/to/.venv/bin/python /absolute/path/to/enforce_inspect.py --stage on-stop --root /absolute/path/to/your/project" }]
    }]
  }
}

With SGS_SANDBOX_ROOT and --root both set, this behaves exactly as described above — fixed root, no ambiguity, easy to reason about in a repo other people also work in.

Option B — Global / user-scope (one install, protects every project)

Best for: your own machine, where you want the gate to apply automatically to any project you open with Claude Code, without configuring each one.

claude mcp add sensory-grounding --scope user -- \
  /absolute/path/to/lucross-sensory-grounding-mcp/.venv/bin/python \
  /absolute/path/to/lucross-sensory-grounding-mcp/server.py

Then in your global ~/.claude/settings.json (not a per-project one):

{
  "hooks": {
    "PostToolUse": [{
      "matcher": "Write|Edit",
      "hooks": [{ "type": "command",
        "command": "/absolute/path/to/.venv/bin/python /absolute/path/to/enforce_inspect.py --stage post-edit" }]
    }],
    "Stop": [{
      "hooks": [{ "type": "command",
        "command": "/absolute/path/to/.venv/bin/python /absolute/path/to/enforce_inspect.py --stage on-stop" }]
    }]
  }
}

Note there's no --root and no SGS_SANDBOX_ROOT in this mode. Without them, the server and the hook both fall back to resolving the sandbox root from the current session's working directory — the server via Path.cwd() at the time it's invoked, the hook via the cwd field Claude Code includes in every PostToolUse/Stop payload. In practice this means: whichever project directory a given Claude Code session is running in becomes that session's sandbox root, automatically, with the exact same path-containment check as Option A — nothing outside that directory is ever reachable.

Verify it after installing, once, in two different project directories — confirm a file edit in project A doesn't show up as pending in project B, and that the Stop hook blocks/releases correctly in each. The cwd-based fallback is the intended mechanism, but exact subprocess cwd inheritance can vary slightly across Claude Code versions/platforms, so this is worth one empirical check on your setup rather than trusting it blindly.

Trade-off to know before you flip this on globally: it now fires in any directory, for any task — including work that has nothing to do with the project you built this for. A throwaway edit to a .png in a random folder will trigger the same block as a real regression. If that gets noisy, Option A (installed only in the repos that matter to you) avoids the false positives entirely.

Either way: any Write/Edit touching a tracked extension (.html .css .svg .pdf .docx .pptx .wav .mp3 .m4a .mp4 .mov) now requires a resolved, improved inspection before the agent can end its turn.

Tell the agent what's expected of it

Add to your project's CLAUDE.md (or equivalent system prompt):

Any change to a visual/audio/video file ends with calling the matching sensor tool, comparing the result against the task's success criteria, and calling mark_inspected with a 0-100 score. Only accept your own change if the new score is strictly higher than the previous one — otherwise revert and try a different approach. Never report a task complete without this.

State model

State is scoped per project, not per session — deliberately. An agent has no reliable way to know its own Claude Code session ID, so pending inspections live in <project>/.sgs_inspection_log.json, keyed by absolute file path, with a persistent per-file score baseline. This also means the gate is meaningfully project-level: "this project has an open, unresolved visual regression" is a fact about the project, not about one conversation.

No SQLite, no external database — one JSON file, human-readable, git-ignored by default.

Why not just prompt the model harder?

Because that's exactly the failure mode the underlying research describes. Self-correction without a deterministic, external verifier reliably underperforms not correcting at all in some tasks (Huang et al.). The fix isn't a better-worded instruction — it's a verifier the model literally cannot argue its way past. That's what the Stop hook's exit code 2 gives you: Claude Code refuses to end the turn and hands the reason back to the model, verbatim.

Design notes

• The critic is the agent itself — not a separate paid vision model. The sensor returns an ImageContent block; whichever model is already running the session evaluates it, inside the same context. Zero marginal API cost.

• Sensors are stateless and know nothing about each other. Swap the rendering engine, the audio backend, anything — the interface doesn't change.

• Sandbox containment is never optional, only its root is configurable. Project-scoped installs pin SGS_SANDBOX_ROOT explicitly; global installs derive it from the current session's working directory instead. Either way, the containment check itself is identical and always enforced — there is no mode where an arbitrary path outside the resolved root is reachable.

License

Apache License 2.0 — see LICENSE. Use it, fork it, ship it inside a commercial product, whatever you need. If you build something interesting on top of it, a mention is appreciated but never required.

Author

Luclino Cruz — github.com/luclinocruz

Built as part of the harness for LuCross, a solo-operated, AI-agent-driven company. If this saved you a debugging session, a star helps other people find it.