Phenomenai

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

No annotations are provided, so the description carries the full burden. It discloses behavioral traits: the scores are 'composite rankings' based on 'centrality, consensus, and usage signals,' and terms are 'ranked into tiers: Hot, Warm, Mild, Cool, Quiet.' However, it lacks details on permissions, rate limits, or response format, leaving gaps for a read operation.

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 appropriately sized and front-loaded: the first sentence states the core purpose, and subsequent sentences efficiently elaborate on the scoring methodology and tier system. Every sentence adds essential context without redundancy, making it highly concise and well-structured.

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 the tool's complexity (a read operation with composite scoring), the description is fairly complete. It explains the output's nature and tier system, and since an output schema exists, it doesn't need to detail return values. However, it could improve by mentioning sibling tools or usage contexts to aid the agent better.

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 0 parameters with 100% coverage, so no parameter documentation is needed. The description adds value by explaining the output semantics (composite rankings and tier system), which compensates for the lack of parameters. Baseline is 4 for 0 parameters, as it focuses on output meaning.

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's purpose: 'Get term interest scores' with the specific output being 'composite rankings showing which terms resonate most.' It uses a specific verb ('Get') and resource ('term interest scores'), but doesn't explicitly differentiate from sibling tools like 'lookup_term' or 'search_dictionary' that might also retrieve term information.

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 no guidance on when to use this tool versus alternatives. It doesn't mention sibling tools like 'lookup_term' (for individual term details) or 'search_dictionary' (for term searches), nor does it specify prerequisites or contexts for usage. The agent must infer usage from the purpose alone.

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