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

Annotations already cover read-only, idempotent, non-destructive, open-world, so the bar is lower, but the description adds real semantic behavior: an UNMEASURED axis is a first-class answer, not an error and not a zero, and the value is never a certification. That interpretation guidance is exactly the kind of context annotations cannot express.

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?

Front-loaded with what the tool is and the no-argument constraint, followed by the field list and the UNMEASURED caveat. The mid-sentence field enumeration (n, accuracy, interval, family, kind...) is a little listy, but every element is informative.

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?

With an output schema present and zero parameters, the description only needs to frame purpose and edge-case semantics, which it does, including the unmeasured-slot rule. The one gap is routing relative to get_axis and the other axis_* siblings.

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?

Zero parameters, so baseline is 4. The description reinforces this by explicitly stating the tool takes no argument, which prevents an agent from hunting for filters, and the empty schema confirms it.

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?

It names a specific resource (the openness axis of the GSPC board), points to the backing URL, and enumerates the fields the axis exposes. However, it never distinguishes itself from the sibling get_axis or from the other axis_* tools, and 'GSPC board' is left as unexplained jargon, so the agent has to infer the split.

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: read this when you want the openness axis. There is no explicit 'use this instead of get_axis when...' and no stated preconditions or exclusions, even though a near-identical sibling exists.

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