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 describes key behavioral traits: it returns low-confidence observations (unlike suggest()), mentions use cases (audits, debugging), and hints at downstream uses (pruning noise with gc() or alias_pattern()). It doesn't cover all potential behavioral aspects like rate limits or auth needs, but provides substantial context beyond basic functionality.

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, then provides usage guidelines with explicit comparisons, followed by detailed parameter explanations, and concludes with return value documentation. Every sentence adds value with no redundancy or fluff.

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 (3 parameters, no annotations, but with output schema), the description is remarkably complete. It covers purpose, usage guidelines, parameter semantics, and behavioral context. The output schema handles return value documentation, so the description appropriately focuses on what's not covered by structured fields.

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 schema description coverage is 0%, so the description must fully compensate. It provides detailed semantic explanations for all three parameters: min_confidence (explains default, inclusive nature, and meaning of values 1, 5, 10), category (lists possible values and empty string behavior), and project (explains filtering by fingerprint and empty string behavior). This adds significant value beyond the bare 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's purpose: 'List every recorded pattern (including seedlings), optionally filtered.' It specifies the verb ('List'), resource ('recorded pattern'), and scope ('including seedlings'), and distinguishes it from sibling tools like suggest() by mentioning it returns low-confidence observations.

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 vs. alternatives: 'Use this for audits, debugging, or when you want to see patterns that have not yet matured.' It explicitly contrasts with suggest(): 'For day-to-day agent guidance, prefer suggest() — it filters to confidence >= 5 automatically and returns a more compact payload.'

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