formal_tests

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

With no annotations provided, the description carries the transparency burden. It discloses the hypotheses tested, critical values, and the computational cost of MEG ('slow'), as well as a precondition for MEG (D=0 + harmonics). It does not state side effects, but for a test tool this is acceptable.

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 well-structured with a clear first sentence, a bulleted list of tests, and a parameter section. Each test description adds essential context, and parameter explanations are concise. No waste.

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 complex tool with no output schema, the description omits what the tool returns (e.g., a report, p-values, a model object). It also lacks broader prerequisites beyond MEG's D=0 condition. The test list is thorough, but the output aspect remains a gap.

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 explain parameters. It does: inp_path is 'path to .inp or .pre file' and run_meg is 'whether to run MEG (slow, default True)'. This fully compensates for the missing schema descriptions.

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 the tool 'Run formal hypothesis tests on a fitted model' and enumerates the specific tests performed (Shin-Fuller, DCD, DCD_f, RV, MEG). This distinguishes it from sibling tools that focus on single tests or broader model actions.

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 gives clear context: to be used on a fitted model, with tests applied 'where applicable to the model structure.' It does not explicitly mention alternatives, but the context is sufficient for an agent to decide when to run this compared to more specialized sibling tools.

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