inspect_dft_dataset

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full behavioral burden. It does disclose one meaningful trait — that the operation is non-mutating with respect to accepted data — which is a genuine safety signal. However, it says nothing about permissions, whether 'incoming' data is scanned, or how results are shaped, leaving notable gaps for an unannotated tool.

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?

A single front-loaded sentence with no filler. It is efficient, though arguably terse enough that it omits information an agent would need rather than trimming genuine redundancy.

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?

An output schema exists, so return values need not be explained, and there is only one optional parameter, which keeps the surface small. Still, for a tool sitting among verify_dft_dataset and freeze_verified_dataset, the description is thin on what inspection yields and how the path argument is interpreted.

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% for the single parameter dataset_path, and the description never mentions it or its default ('data/incoming'). With the schema silent and the description not compensating, the agent has no textual guidance on what the path should point to.

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+resource ('Inspect QE outputs') and adds a scope qualifier ('without mutating accepted data') that distinguishes it from mutation siblings like freeze_verified_dataset. It does not, however, distinguish it from the closely related verify_dft_dataset or explain what 'inspect' covers versus 'verify'.

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?

No when-to-use guidance, no exclusions, and no routing to alternatives. The phrase 'without mutating accepted data' hints that this is a read-only look at incoming data, but the agent gets no explicit signal about when to choose inspect_dft_dataset over verify_dft_dataset or get_pipeline_status.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.