serringa
Registered
Thread Starter
- Joined
- May 18, 2026
- Posts
- 3
More
- Preamp, Processor or Receiver
- yamaha RX-a1060
- Front Speakers
- canton vento 870 dc
- Center Channel Speaker
- canton vento 855
- Surround Speakers
- canton vento 870 dc
- Subwoofers
- canton as225sc (csp300 module), canton power sub 10
- Screen
- LG G5 77
Hi, this is my first post. I started with a UMIK and REW a few months ago, and since then I started building a REW API / Claude MCP tool.
I understand that AI and audio do not go together for many people, and I get the scepticism. I am trying to use AI in a controlled way: not to make things up or automatically “fix” the room, but to help read measurements, check evidence, compare data, and avoid missing important details.
If some more experienced REW users could give me a clue whether this is something worth continuing, or whether I should just throw it in the bin, I would really appreciate it.
The first text is a public/forum-style explanation of the project: a local REW Pro MCP analysis workflow that lets an AI assistant inspect REW measurements through structured data instead of only screenshots. It explains the goal, limits, and intended read-only use.
The second text is a more technical workflow report. It describes how the tool should actually perform an analysis: preflight, measurement identity checks, fingerprinting, evidence reconcile, evidence audit, compare intent, interpretation, and report-pack export.
The tool can already do early versions of:
- measurement fingerprinting
- evidence/registry reconcile
- structured measurement comparison with evidence labels
- compact report-pack export with UUIDs, metadata, fingerprints, notes, and integrity hash
- separating raw evidence from derived interpretation
It is not meant to be automatic room correction, and it does not replace REW knowledge or acoustic judgement.
I would like feedback on:
- what is good about this approach
- what is risky or overcomplicated
- what evidence checks are missing
- what should remain manual
- whether this would be useful for serious REW users
- whether the workflow is clear enough for others to trust or criticize
```
I understand that AI and audio do not go together for many people, and I get the scepticism. I am trying to use AI in a controlled way: not to make things up or automatically “fix” the room, but to help read measurements, check evidence, compare data, and avoid missing important details.
If some more experienced REW users could give me a clue whether this is something worth continuing, or whether I should just throw it in the bin, I would really appreciate it.
The first text is a public/forum-style explanation of the project: a local REW Pro MCP analysis workflow that lets an AI assistant inspect REW measurements through structured data instead of only screenshots. It explains the goal, limits, and intended read-only use.
The second text is a more technical workflow report. It describes how the tool should actually perform an analysis: preflight, measurement identity checks, fingerprinting, evidence reconcile, evidence audit, compare intent, interpretation, and report-pack export.
The tool can already do early versions of:
- measurement fingerprinting
- evidence/registry reconcile
- structured measurement comparison with evidence labels
- compact report-pack export with UUIDs, metadata, fingerprints, notes, and integrity hash
- separating raw evidence from derived interpretation
It is not meant to be automatic room correction, and it does not replace REW knowledge or acoustic judgement.
I would like feedback on:
- what is good about this approach
- what is risky or overcomplicated
- what evidence checks are missing
- what should remain manual
- whether this would be useful for serious REW users
- whether the workflow is clear enough for others to trust or criticize
```
