time_series_decompose

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

With no annotations, the description carries the full burden and discloses important behavioral details: the full output is written to an .npz file under output_dir(), and returned arrays are downsampled to at most 2000 points. It also notes a validity constraint on 'period'. Missing only explicit mutation/read-only nature or expected return structure.

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 concise sentences, front-loaded with the core action and followed by key operational constraints. No extraneous text, though the formatting with backticks is slightly noisy.

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 no annotations, no output schema, and 0% parameter schema coverage, the description covers the essential file-saving/downsampling behavior and period constraint, but leaves the 'model' parameter's meaning and the 'series' input format undocumented, which an agent would need to call 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 coverage is 0%, so the description must compensate. It clarifies the constraint on 'period' (at least 2, smaller than series length) and implies the shape/output of results, but says nothing about 'model' (e.g., additive vs multiplicative) or the accepted formats for 'series'. Partial compensation.

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?

States a specific verb (decompose) and resource (a series into trend, seasonal, residual). Distinguishes from siblings like arima or fft_analysis by naming the exact decomposition operation, though it doesn't explicitly contrast with them.

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?

Provides a constraint on the 'period' argument but gives no guidance on when to use this tool versus alternatives such as fft_analysis or filter_signal, nor any typical use context.

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