Content
72%Weight 40%Scale 1-5Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
The body is highly actionable with executable code and a clear workflow, but it lacks a deploy/validation checkpoint and references an examples/ directory that is absent from the bundle.
Suggestions
Add a validation checkpoint after `databricks bundle run` (e.g., check app logs or confirm the MCP tools list is populated) to turn the deploy step into a validate-fix-retry loop.
Create the referenced `examples/` directory with the listed YAML/md files, or inline the few most-used snippets so no referenced path is broken.
De-duplicate the value_from guidance: state the 'must match a resource name' rule once and remove the redundant 'IMPORTANT' and 'Critical' restatements plus the notes bullet.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | Mostly lean with tight code blocks and a compact table, but the value_from rule is restated three times and deploy+run is repeated in the notes, so minor trimming is possible. | 4 / 5 |
Actionability | Provides copy-paste-ready Python with real imports, complete databricks.yml YAML blocks, concrete CLI commands, and specific error-handling kwargs covering the common cases. | 5 / 5 |
Workflow Clarity | A clear three-step sequence (add server, grant access, deploy+run) exists, but there is no explicit validation or verification checkpoint for the deploy operation, capping this dimension per the batch-operation guideline. | 3 / 5 |
Progressive Disclosure | Structure and signaled one-level-deep references to an examples/ directory are good, but the referenced examples/ files do not exist in the bundle, breaking navigation. | 3 / 5 |
Total | 15 / 20 Passed |