meg_reformulate

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

No annotations are provided, so the description carries the full burden. It clearly states that the tool 'writes the reformulated .pre/.out to output_path', 're-estimates', and 'shows the model equation + diagnosis'. It also explains the behavioral difference between with_witness=True and False, including the consequence of over-differencing (inflated sigma, exploded Q-test). A minor gap is that it does not explicitly warn about overwriting existing output files, but overall transparency is strong.

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 longer than average, but the complexity of the tool justifies the length. It is well-structured: an opening definition, then a detailed explanation of the two modes, iterative guidance, and a parameter list. While not as terse as the highest-caliber examples, every sentence adds value and the structure is logical.

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 tool's complexity, the lack of output schema, and no annotations, the description is remarkably complete. It explains the prerequisite (MEG conclusion), the underlying math (AR_f unit root, MA_f witness), the output side effects, and the intended follow-up workflow (formal_tests). This fully equips an agent to invoke 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?

With 0% schema description coverage, the description must explain each parameter. It does so in a dedicated Parameters section, covering inp_path, freq, output_path, base_pre_path, and with_witness with meaningful context (e.g., 'ifadf[freq]=1', 'the free invertible MA_f testigo'). This goes far beyond the bare schema.

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 opens with a specific verb-resource combination: 'Reformulate the model for STOCHASTIC seasonality at frequency `freq`' and immediately ties it to the MEG workflow, distinguishing it from sibling tools like formal_tests or identification_analysis. The scope is clear and unambiguous.

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 explicitly states when to use the tool ('after the MEG... has concluded stochastic there'), provides clear guidance on the with_witness parameter ('Use it only to inspect the bare over-differenced residuals' for False), and, for multiple stochastic frequencies, instructs to 'call iteratively (strongest first)'. It also references the next step (`formal_tests'), giving full contextual usage.

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