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

Beyond the annotations (readOnly=true, destructive=false), the description discloses methodology: it separates LIVE catalog decay from FROZEN dated survival study, counts only vendor-published links, and updates daily. This adds significant behavioral context about the data source and processing.

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 long but well-structured, starting with usage, then datasets, then exclusions. Every sentence provides useful information without redundancy. It is slightly verbose for the sake of thoroughness, but earns its length.

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 complex tool with two datasets and no output schema, the description is complete: it explains both datasets, their update frequency, data sources, counting methodology, and limitations. The annotations cover safety, so no additional behavioral disclosure is needed.

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 input schema has one parameter (response_format) with 100% coverage including descriptions for both enum values. The tool description does not add any additional parameter semantics, so it relies on the schema, which fully covers it. Baseline 3 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 what the tool does: it provides AI tool mortality statistics including failure rates, decaying categories, and survival studies. It explicitly distinguishes from sibling tools by noting it is not for checking one specific tool (check_tool_status) or predicting a specific tool's future (viability_score).

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 explicit use cases ('Use this when the user asks...') and exclusions ('Not for: checking one specific tool (use check_tool_status) or predicting a specific tool's future (use viability_score)'). It also clarifies that decay rates describe categories and cohorts, not individual products, which is a critical boundary.

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