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

Annotations already declare this read-only and idempotent, and the description adds substantial behavioral context beyond that: it reveals that all six default layers run large, two truncate, that row_limits indicates whether truncation occurred, and that some layers are opt-in. This gives the agent accurate expectations about response completeness and performance.

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?

Despite being longer than average, every sentence carries distinct, non-redundant information: window defaults, layer behavior, truncation signals, and opt-in layers are all covered efficiently. The core purpose is front-loaded, and the additional detail is justified because the input schema provides no property descriptions.

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?

The description covers the tool's purpose, parameter semantics, defaults, performance caveats, and opt-in behavior. An output schema exists, so return-value documentation is unnecessary here. Given the tool's complexity and the zero-coverage schema, this description leaves no important calling detail unaddressed.

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%, but the description fully compensates: it defines days_back as an integer range 1..90 with a default of 7, and it enumerates exact layer names, comma-separated formatting, and which reference layers require explicit opt-in. This is more than enough for an agent to construct valid arguments.

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 identifies the tool as retrieving geopolitical events over a configurable look-back window, naming the signal types (conflict, chokepoints, sanctions). It is specific about the resource and scope, but it does not explicitly contrast itself with sibling tools like get_risk_radar or get_catalysts, so it falls short of full sibling differentiation.

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 gives strong in-tool usage guidance: it explains how to pass layers, that one or two layers return deeper data, and that three reference layers are opt-in. However, it never states when to prefer this tool over alternatives or when not to use it, so the when-to-use guidance is only implied by the tool's domain.

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