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

Annotations are empty, so the description carries the full burden. It discloses generation frequency ('generated every hour from live data'), content structure ('narrative analysis... recommended watch items'), and output suitability ('structured briefs... Slack channels'). It does not detail auth needs or response envelope, but this is a read-only GET-style tool and the description is sufficiently transparent.

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 five sentences, each serving a distinct purpose: core function, brief contents, synthetic nature, target audience, and output format. It is front-loaded with the main verb and avoids redundancy, making it 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?

For a tool with no parameters and no output schema, the description is remarkably complete. It names all five dimensions, explains what each brief contains (current conditions, drivers, risks, watch items), and notes the update frequency. An agent can accurately predict the tool's behavior and return value, though exact response formatting is unspecified.

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, so the schema coverage is vacuous. The description adds no parameter-specific meaning, but it correctly contextualizes the response content. Baseline 4 for zero params 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 clearly states the tool 'Get AI-generated intelligence briefs for each supply chain dimension', listing the specific dimensions (energy, materials, transportation, macro, manufacturing) and emphasizing narrative synthesis rather than raw data. This distinguishes it from data-focused sibling tools, though it doesn't name them.

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 gives clear usage context: 'Designed for decision-makers who need a quick read' and explicitly contrasts with raw data ('These are not raw data'). It implies when to use this tool over data-monitoring tools, but does not explicitly name alternative tools or state when not to use it.

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