find_hot_paths

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

No annotations exist, so the description must compensate. It explains the ranking algorithm but does not mention whether the tool is read-only, has side effects, or requires specific permissions. The behavior is implied but not fully disclosed.

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 short, focused sentences with no fluff. The first states the purpose, the second explains the core metric. Every word adds value.

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 simplicity (one optional parameter) and the presence of an output schema, the description fully covers what the tool does, how to use it, and what to expect. No gaps.

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 only parameter (top_n) is fully documented in the input schema (100% coverage). The description adds no extra meaning beyond what the schema already provides, so baseline score of 3 is appropriate.

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 verb 'Find' and specifies the resource 'high-leverage optimization targets', with a concrete formula (complexity * call frequency). This uniquely differentiates it from sibling tools like get_complexity or get_call_graph.

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?

Implicitly suggests use when prioritizing optimization efforts. While it doesn't list when not to use or name alternatives, the context from sibling tools makes the distinction clear enough.

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