instinct

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively explains the ranking logic ('patterns that have accumulated the most observations in the last N days, regardless of total confidence'), provides context about how new vs. mature patterns are treated, and describes the return format. However, it doesn't mention potential limitations like rate limits or authentication requirements.

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 well-structured and appropriately sized. It begins with the core purpose, provides usage context, contrasts with alternatives, documents parameters with helpful explanations, and describes returns. Every sentence adds value with zero wasted text.

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 moderate complexity (2 parameters, no annotations, but with output schema), the description is complete. It covers purpose, usage guidelines, parameter semantics, and behavioral context. The output schema handles return value documentation, so the description appropriately focuses on operational context rather than repeating structured data.

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 description adds significant meaning beyond the input schema, which has 0% description coverage. It explains both parameters in detail: 'days: Window size in days. Default 7. Smaller = what's hot now; larger = what's been steady' and 'limit: Max patterns to return, sorted by observation count descending. Default 10.' This provides crucial context about how parameter values affect results.

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: 'Rank patterns by observation velocity in a recent window.' It specifies the verb ('rank'), resource ('patterns'), and key metric ('observation velocity'), and distinguishes it from sibling tools by contrasting with 'list_instincts' for all-time leaderboards.

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 explicit guidance on when to use this tool versus alternatives: 'Use this to surface what the agent is currently learning about... For all-time leaderboards use list_instincts(min_confidence=10); for recent activity use this.' It clearly defines the use case and names a specific alternative tool.

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