towerwatch_query_metrics

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

Annotations only declare readOnlyHint and destructiveHint. The description adds meaningful behavioral context by explaining data_status semantics: 'empty_window' as a true negative versus 'not_collected' as no evidence, which is critical for interpreting results. It also discloses downsampling behavior and per-series output.

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?

Every sentence earns its place: purpose, usage selection, return format, and an important caveat about data_status. The structure is front-loaded and the caveat is placed where it will be read before acting on numbers.

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 read-only query tool, the description covers when to use it, what it returns, and the crucial data_status interpretation. Pagination and request shape are documented in the schema, and there is an output schema, so the description is complete enough for an agent to call it correctly.

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 the description does not explain the primary request parameters such as site, start, end, metric_group, or pagination. It only implies per-metric and downsampled behavior. The nested schema helps, but the description itself does not compensate for the coverage gap.

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 it returns raw time-series data points as downsampled [timestamp, value] pairs per metric, and explicitly distinguishes itself from analyze_window by saying this tool is for actual numbers while the sibling is for judgments. This gives an agent a clear, specific understanding of the tool's function.

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?

It explicitly says to pick this tool when actual numbers are needed and the agent will do its own reasoning, and directs users to analyze_window when they want a judgment about a window. This is clear when-to-use and when-not-to-use guidance.

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