duplicates

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

Annotations already mark this as read-only, idempotent, and non-destructive. The description adds meaningful behavioral detail by specifying the output categories (exact, renamed, structural), the canonical pick, remapped copies, and confidence score, which goes beyond the annotations.

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, information-dense sentence that front-loads the usage context and then describes the output structure without waste. Every clause contributes: when to call, what it returns, and how results are categorized.

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 parameterless read-only tool, the description is complete. It covers the trigger conditions, the analytical scope (duplicate implementations), the three detection flavors, and the actionable output (canonical pick, remap targets, confidence). No output schema exists, so this description adequately fills that gap.

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 tool has zero parameters, so there is no parameter burden on the description. The description fully conveys what the operation analyzes and returns, satisfying the baseline for parameterless tools.

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 states a clear purpose: detect duplicate implementations before adding a helper or when consolidating logic. It distinguishes itself from siblings like find_symbol and dead_code by describing exact, renamed, and structural duplicate groups with canonical picks and confidence scores.

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 explicit when-to-use scenarios: before adding a helper to check for existing duplicates, or when consolidating repeated logic. It does not name when-not-to-use or discuss alternatives among siblings, but the context is clear enough for an agent to select it appropriately.

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