List employee tables

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

Annotations already declare the operation safe/read-only; the description adds meaningful behavioral context by stating that certain tables and money-type columns are excluded by policy and will not appear. It also sets expectations for the response content (alias + columns), which goes beyond the annotations.

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 main purpose is front-loaded, followed by one sentence of policy exclusions and one routing pointer to a sibling tool. Every sentence earns its place, with no redundancy relative to the schema or annotations.

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 no-input discovery tool, the description explains what will be returned, what is excluded, and how to use the results with bamboohr_table_rows. The annotations cover safety and open-world behavior, so nothing needed to call it correctly is missing.

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?

The tool has zero parameters and 100% schema coverage, so there are no parameter semantics the description must explain. This matches the baseline for a no-parameter tool.

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 names the exact action ('List'), the resource ('tabular fields this server may read'), and the delivered content ('table alias and its columns'). It also distinguishes the tool from sibling bamboohr_table_rows by explaining how the alias is used downstream.

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 clarifies when the tool is appropriate for job history, employment status, and custom tables, and explicitly states policy exclusions so an agent won't expect compensation, bank, or health tables. It names bamboohr_table_rows as the follow-up, though it doesn't explicitly contrast it as the alternative for row-level data.

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