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

Beyond the readOnly/openWorld annotations, the description discloses substantial behavior: the script uses only the standard library, makes no network calls, uses fixed seeds, supports .csv and .json, and emits counts with a fingerprint but no rows. It also documents the error path (returns spec errors instead of the script), which is exactly the kind of context annotations cannot carry.

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?

The content is dense and nearly waste-free, but the opening clause "Returns measure.py with a confirmed spec written in" is a run-on whose syntax obscures the actual object being returned. Front-loading is attempted but the phrasing gets in the way.

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?

No output schema exists, so the description must define the return value, and it does: either the script text or the spec errors. With a nested object parameter and a fixed-read annotation set, the remaining gap is mostly operational (is the script returned as text or a file, and how it is meant to be executed).

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 coverage is 100%, so both parameters are already documented, including the optional columns reconciliation parameter. The description adds no additional meaning about spec shape or what happens when column checking is omitted, so it sits at the baseline.

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?

The description uses a specific verb and resource ("Returns measure.py") and explains concretely what the artifact does: a fixed Python script that counts what each check needs and prints counts plus a fingerprint, never rows. That distinguishes it from sibling tooling like show_audit or measure_in_app, though no sibling is named explicitly.

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?

Usage is only implied: the spec parameter is described in the schema as coming from start_audit as confirmed by the person, so the ordering is inferable. The description itself never states when to call this versus start_audit or measure_in_app, and there are no exclusions or prerequisites spelled out.

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