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

With no annotations, the description carries the full burden. It adds valuable behavioral context: the metric is 'community-reported', represents a 'median', and is 'position not probability' — clarifying what the number does and does not mean. This goes beyond a simple read hint.

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?

Two concise sentences with no filler. The key term is front-loaded, and the definition is provided efficiently. The brief clarification about community-reported and position-not-probability earns its place.

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 tool has no output schema, so the description should explain return values. It defines the metric but does not explicitly state the return format (e.g., number, ratio, percentage) or any caveats like data availability. It is minimally adequate for a simple getter.

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 100% and the city parameter is adequately described. The description adds minor context by indicating the benchmark is city-specific, but does not provide additional meaning beyond the schema.

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 gets a community-reported pre-spend benchmark for a city, with a specific metric definition. It doesn't explicitly distinguish from siblings like get_market_median, but the unique concept of 'pre-spend ratio' is specific enough.

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?

No explicit when-to-use guidance or alternative tool comparisons are provided. The description implies usage for checking pre-spend benchmarks per city, but does not explain when it should be preferred over related tools.

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