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

With no annotations provided, the description carries the full burden of behavioral disclosure. It thoroughly explains the output structure: per-pillar scores, trend direction, and contributing data points. It doesn't mention potential caveats like data freshness or read-only nature, but for a breakdown/reporting tool, the described behavior is sufficiently transparent. Since there are no side effects or complex interactions, this is adequate.

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 long, front-loaded with the primary purpose, followed by specific pillar details and a use case. Every word contributes meaning without redundancy. It is efficient and well-structured.

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?

Given the tool has no parameters and no output schema, the description is the sole source of information. It fully specifies what the user will receive: scores for each GDI pillar, trend direction, and driving data points, with examples of subcategories. It also clearly states the intended application (diagnosing which risk category is elevated). This is complete for a tool of this simplicity.

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 input schema has zero parameters, so the baseline is 4. The description correctly does not attempt to explain parameters that don't exist. It adds semantic value by clarifying that the tool returns a categorical breakdown, which is inherently parameterless, so there is no ambiguity about inputs.

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 clearly states the tool's function: 'Get detailed breakdown of supply chain disruption risk by category.' It specifies the exact content: individual scores for each GDI pillar with subcategories, trend, and driving data points. This distinguishes it from sibling tools like 'get_energy_breakdown' or 'get_port_congestion_trends' which focus on singular areas, while this provides a cross-category diagnostic view.

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 explicitly identifies the target user and scenario: 'Essential for supply chain managers who need to diagnose which risk category is elevated and why.' This provides clear context for when to use the tool. However, it does not name alternative tools or explicitly state when not to use it, but the phrasing implies a diagnostic need rather than a specific single-area deep dive.

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