Run data quality audits on a specific model or all models, with optional start and end bounds for the data to check.
Run audits for a model (or all models if omitted). start/end bound the data checked.
| Name | Required | Description | Default |
|---|---|---|---|
| end | No | ||
| start | No | ||
| model_name | No |
Changes observed during successful MCP inspections.
v0.1.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, so the safety profile is known. The description adds that start/end bound the data checked, but it does not disclose what an audit produces, whether it can be long-running, or any other behavioral characteristics beyond the annotation. It is adequate but not rich.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two short sentences, no filler, with the core purpose and the key parameter semantics both front-loaded. Every clause earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with three optional parameters and no output schema, the description covers the selection scope and the two bounds, but it does not describe the nature of the audit results or clarify the expected format of start/end. It is minimally sufficient but leaves meaningful gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description carries the burden of explaining the parameters. It does add real meaning: model_name selects the model (and omitting it means all models), while start/end bound the data checked. This goes beyond the bare schema, though it still lacks format or interaction details.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
States a specific verb and resource: run audits for a model, with the all-models behavior clearly scoped. The mention of start/end bounds adds precision and distinguishes it from generic sibling tools like run_test or run.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies when to use the tool (run audits for a model or all models) and clarifies the effect of omitting model_name, but it gives no explicit guidance about when not to use it or which sibling alternative to prefer. Usage context is present but alternatives and exclusions are absent.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.