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

Annotations already indicate read-only, non-destructive behavior, so the bar is lower. The description adds useful behavioral context by naming the categories of values returned and advising use of exact values. It doesn't describe response format or edge cases, but for a zero-parameter lookup this is sufficient.

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 that conveys purpose, scope, and usage guidance with no filler. 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 zero-parameter, non-destructive reference lookup, the description gives enough context to know when to use it and what it returns. It could mention output shape or possible empty/unavailable values, but this is a minor gap given the tool's simplicity.

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?

There are no parameters, so the baseline is 4. The description fully covers the semantic space by listing what kind of values can be looked up, leaving no parameter ambiguity since none exist.

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 ('look up') with a clear resource ('valid accounting values') and concrete examples of what those values include. It is clear, though it does not explicitly distinguish itself from sibling lookup tools like get_filter_options.

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 provides clear usage context: call this before classifying or filtering and use the returned exact values instead of guessing. It does not mention alternative tools or exclusions, but the intended timing is explicit.

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