get_frustration_breakdown

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

With no annotations, the description carries the full burden. It discloses the metric types covered, the 1/2/3-day window constraint, and that the result is a ranked JSON structure, which is real behavioral context. It omits auth/permission requirements (the token is only called 'optional'), rate limits, and any statement about data freshness or cost.

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?

Front-loaded purpose sentence followed by clearly separated Args and Returns blocks; the metric examples and value constraints each earn their space. It is slightly padded by restating parameter names that already appear in the schema.

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 four-parameter read-only analytics tool with an output schema already covering the return shape, the description supplies the missing parameter semantics, defaults, and value constraints. What remains absent is operational context (auth, rate limits, when to prefer this over sibling insight tools), which is a modest gap rather than a blocker.

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 description coverage is 0% and there are no enums in the schema, so the description must compensate — and it largely does, spelling out project_id, the five allowed dimension values with a default of 'URL', the restricted num_of_days values, and token optionality. The token entry is thin (no guidance on when it is needed or where it comes from), keeping this short of a 5.

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 gives a specific verb ('break down') and resource ('user frustration metrics') and even enumerates the underlying metrics (rage clicks, dead clicks, quick backs), so the agent knows exactly what this produces. It never names or contrasts with the plausible sibling get_ux_insights, so sibling differentiation is left to inference.

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?

Usage is implied by the 'across a specific dimension' framing and the documented defaults, which tell the agent this is a read-only analytical breakdown. There is no explicit when-to-use, when-not-to-use, or routing guidance relative to get_ux_insights or get_js_errors.

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