fiscal_by_institution

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

Annotations already declare readOnly/idempotent/non-destructive. Description adds data availability 'since 2007', allowed variable values, and a note about empty results, which are useful behavioral details.

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?

Four sentences, no fluff. Front-loaded with core purpose, followed by example, allowed values, and edge-case 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?

Output schema exists, annotations cover safety. The description covers purpose, usage, data range, variable options, and empty-result behavior. It could mention how to discover department names, but sibling fiscal_list_dimensions likely fills that gap.

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 coverage is 0%, so description must compensate. It explicitly lists the allowed values for variable, and the example hints at year/department usage. However, it does not explain year_from/year_to defaults or department name format, leaving some ambiguity.

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?

Clearly states it returns federal spending by department/administrative unit, and provides a concrete use case. Distinguishes from siblings by emphasizing institution-level filtering.

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?

Explicitly gives a use case ('compare personnel, IT or external-services spending across departments') with an example. Does not name alternative tools, so not a 5, but the guidance is clear.

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