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

Annotations already cover the safety profile (readOnlyHint, idempotentHint, destructiveHint=false, openWorldHint=false), so the bar is lower. The description adds real behavioral value: results are ranked by recent lobbying activity, and each record includes the current firm plus the matching former positions. It stops short of detail on result volume or pagination, but it meaningfully supplements 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?

Three tightly packed sentences with no filler: the core dataset is front-loaded, the enumeration of office types clarifies scope, and the optional issue narrowing comes last. Every clause carries information the agent needs.

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 no output schema, the description takes on the burden of describing returns and does so adequately (ranking, current firm, matching former positions). Combined with annotations that cover safety and a fully documented two-parameter schema, the definition is close to complete, missing only minor details like result limits.

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 office and issue are already documented with examples in the schema. The description adds only light framing ('Optionally narrow to lobbyists active on one issue') without new syntax, matching rules, or format guidance, so the baseline 3 is appropriate.

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 states a precise resource and scope: registered federal lobbyists who previously worked for a named government office (member, committee, chamber, White House, agency). The 'revolving door' framing and the phrase 'disclosed working for a given government office' clearly separate this former-employment lookup from sibling tools like who_lobbies_agency or who_lobbies_on, which concern current lobbying activity.

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 implies its use case by defining the dataset (people who moved from government to lobbying) and notes an optional issue narrowing, but it never states when to choose this tool over alternatives such as get_lobbyist or who_lobbies_agency, nor any exclusions or prerequisites. Usage must be inferred.

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