get_intelligence_briefs

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

With no annotations, the description carries the full burden and does well by disclosing that briefs are AI-generated, synthesized from live data on an hourly cadence, and contain narrative analysis with key drivers, risks, and watch items. It does not mention potential limitations, such as possible AI inaccuracies or how many briefs are returned, but the core behavioral nature is clearly conveyed.

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 and is front-loaded with the core purpose. Each sentence adds useful context—content type, cadence, target audience, and output suitability—but it is slightly verbose compared to truly concise examples. All sentences earn their place, but the description could be tightened without losing value.

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 no parameters, no annotations, and no output schema, the description is quite complete: it explains what the briefs contain, their cadence, target users, and expected output format. It lacks explicit comparison to closely related siblings like get_supply_chain_weekly_brief, but the hourly cadence and dimension-specific focus provide enough context for an agent to select and invoke the tool.

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?

There are zero parameters, and the schema is empty, so the parameter semantics are inherently fully covered by the schema. The description adds useful context about the five dimensions covered but does not need to explain parameters since none exist. The baseline score of 4 for a no-parameter tool 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 returns AI-generated intelligence briefs for specific supply chain dimensions (energy, materials, transportation, macro, manufacturing), using a specific verb and resource. It further distinguishes itself from raw-data tools by emphasizing these are synthesized analytical summaries, which helps differentiate it from siblings like commodity_price_monitor or get_freight_rate_observations.

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 provides clear context: it is designed for decision-makers needing a quick synthesized read on each supply chain dimension and explicitly notes these are not raw data. However, it does not name alternative tools or state when not to use this tool, so it falls short of an explicit when/when-not guidance.

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