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

The readOnlyHint, idempotentHint, and destructiveHint annotations already establish a safe read operation. The description adds context about active job counts and the semantic meaning of returned codes/slugs, going beyond the annotations without contradicting them.

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 concise, front-loaded sentences with no filler. The first states the main action and scope; the second provides a direct actionable usage note.

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?

The tool is simple with optional parameters, full schema coverage, an output schema, and annotations. The description covers purpose, hierarchy levels, counts, and downstream usage—everything needed for an agent to select and invoke the tool correctly.

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 100%, so the schema fully documents all three parameters. The description's mention of codes/slugs and search_jobs filters is more about output usage than parameter meaning, adding limited value for parameter selection.

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 ('List') and resource ('location hierarchy'), names the exact levels (countries, regions, cities), and mentions active job counts. This clearly distinguishes it from sibling tools like list_companies or search_jobs.

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 second sentence explicitly directs the agent to use the returned codes/slugs for the 'state' and 'city' filters in search_jobs, providing a clear use case. It doesn't state when not to use it or compare against alternatives, but the context is strong.

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