trace_dependency

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

With no annotations, the description reveals that lessons store dependencies via 'depends_on' field and that the 'mark_review' parameter triggers side effects. This provides useful behavioral context beyond the basics.

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?

Two concise sentences, front-loaded with the core purpose. Every sentence adds value without redundancy or extra words.

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 tool without output schema, the description covers usage, parameters, and side effects well. Minor gap: return format is not mentioned, but the tool is simple enough that this is not critical.

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?

Schema coverage is 100%, so baseline is 3. The description adds meaningful examples for 'dependency' (e.g., 'node:>=20') and clearly explains the 'mark_review' flag, enhancing understanding.

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 starts with 'Causal Chain — find all lessons that depend on a given prerequisite,' which uses a specific verb and resource. It clearly distinguishes from siblings like causal_trace by focusing on dependency tracing and gives concrete examples.

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 states 'When a dependency changes (new version, different provider, new OS), call this to see which lessons need review,' providing clear when-to-use guidance. It lacks explicit exclusions or alternative tool recommendations but is sufficient.

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