Jev Review (MCP Plugin)

Overview & result

Gives coding agents continuous feedback as they write code. The agent queries Jev across specified quality metrics, receives calibrated scores, and refines implementation before returning to user.

From the original post

built `jev-review` @typesafeai

it's an experimental, local-first MCP plugin that gives coding agents a score quality feedback loop across different metrics.

agents call jev while they work, get scored, make improvements, and repeat the loop

try below 👇

How Jev fits in the loop

1. Coding agent writes code candidate.
2. Jev Review MCP tool scores code against lint, readability, and logic criteria.
3. If score passes threshold, finish; otherwise agent fixes identified issues.

How to reproduce

1. Inspect the source implementation at https://x.com/niazmorshed_/status/2100465662867218857.
2. Configure local Jev API credentials and register required tool/model bindings.
3. Execute test prompts and observe decision latency and routing outputs.

Why this build matters

Automates quality gates inside autonomous coding loops without expensive LLM review prompts.

Reported performance

Latency: 129 rows in ~1 s

Limitations

• Metrics are author-reported from the initial release unless independently verified.
• Requires access to the respective agent framework or runtime environment.
• Generative model execution remains external to the Jev decision step.

Patterns