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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint=false, so the safety profile is covered. The description adds useful behavioral context by clarifying it maps definitions to datasets and supports both variable-name and keyword queries, though it does not detail return format or edge cases.

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 description is two sentences with no filler. It front-loads the core purpose, provides concrete examples immediately, and ends with directly actionable use cases.

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 simple two-parameter lookup tool, the description combined with the schema is sufficient for correct invocation. It even explains the two main agent intents—interpreting a known field and finding a field for a question—though it does not explicitly describe the response structure.

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 both query and limit are already fully documented. The description re-emphasizes that query can be a variable name or definition words Coinciding with schema documentation, but it does not add meaning beyond the schema or its examples.

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 ('Explain') and a clear resource ('column names used in WHO tuberculosis data'), and it gives concrete examples like e_inc_100k and c_newinc. It also distinguishes the dictionary tool from likely data-fetching siblings by emphasizing definitions and dataset attribution rather than numeric results.

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?

It explicitly states when to use the tool: 'Use to interpret a WHO TB field before reporting a number, or to find which variable answers a question.' This gives clear context, though it does not explicitly name alternative sibling tools such as who_tb_estimates or who_tb_budget.

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