tsf_describe_series

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

Annotations already declare readOnlyHint, idempotentHint, and nondestructive nature. The description adds 'Cheap -- returns in under a second' and 'Read it as evidence, not as an answer,' providing behavioral context 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?

The description is concise with five sentences, front-loaded with purpose, and every sentence adds value. No redundancy or fluff.

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 existence of an output schema (not shown but present), the description covers the semantic meaning of the features and how to interpret them. It also provides cost and time estimates, making it self-contained.

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 input schema already has detailed descriptions for all three parameters (handle, max_series, response_format). The tool description focuses on output features and usage advice, not parameter details. Since schema coverage is high, a baseline 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 starts with a clear verb-resource pair: 'Compute the per-series features that decide which model family to try.' It lists the specific features computed, distinguishing this diagnostic tool from siblings like tsf_forecast or tsf_load_series.

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 provides explicit decision rules: 'high seasonal_strength argues for SeasonalNaive or AutoETS; a high pct_zero argues for the intermittent-demand models...' and positions the tool as 'evidence, not an answer.' This clearly guides when and how to use the tool.

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