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

With no annotations, the description carries the full behavioral burden. It discloses useful scope details—90-day window, daily granularity, and returned metrics—but does not state read-only status, permissions, rate limits, pagination, currency, or other operational behavior.

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?

A single, front-loaded sentence with no wasted words. It communicates scope, granularity, key metrics, and optional filtering efficiently.

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 definition gives enough context to understand the core data returned, but with bare schemas, no annotations, and no output schema, it leaves out operational details such as parameter formats and when to choose this tool over sibling market tools.

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 description coverage is 0%, so the description must compensate. It correctly identifies brand and model as required and fuel as optional, covering all three parameters semantically, but it does not specify accepted formats or values for fuel, brand, or model.

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?

States a specific verb and resource: 90-day daily price history for a brand+model, optionally filtered by fuel. It also names the returned metrics (median, avg, count), so the purpose is clear, though it does not explicitly differentiate itself from sibling tools like get_market_trend or estimate_valuation.

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?

Provides no guidance on when to use this tool versus alternatives, nor any prerequisites or exclusions. The only implied usage is the required brand+model inputs and optional fuel narrowing.

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