fit_time_series

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

No annotations are provided, so the description must disclose behavior. It states that the ranking does not modify the workbook and that writing the projection creates a CSE-entered ARRAY formula. It also notes the limitation on parameter uncertainty. This is clear and honest about side effects.

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 dense but efficient, front-loading the purpose and technical details without unnecessary words. It is well-structured with clear sections. A slight improvement could be breaking into paragraphs, but it remains concise.

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 complexity of the tool (multiple families, ranking, optional projection, array formulas) and the presence of an output schema, the description is thorough. It covers all essential aspects: ranking, writing projections, dry run, and unsupported features. No gaps remain.

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 has 100% coverage, so the baseline is 3. The description adds value by explaining the default for 'families' (a broad 12-family set), the effect of 'target_range' and 'dry_run', and the criteria options. This enriches the schema beyond mere field names.

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 it fits time-series models to historical data and ranks them by goodness of fit. It lists specific model families (GBM, AR1, etc.) and distinguishes from distribution fitting siblings like 'fit_distribution_to_data' and 'fit_and_rank_distributions'.

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 explains optional parameters like 'target_range' and 'dry_run', and notes when to use them. It also mentions that parameter uncertainty is not supported. However, it does not explicitly state when not to use this tool or provide alternatives for other tasks.

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