test_hypothesis

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

Annotations already indicate readOnlyHint=true and destructiveHint=false, but the description adds no behavioral details beyond that. It does not mention return format, potential errors, or side effects, relying entirely on 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 extremely brief but cryptically worded, with run-on phrasing and unclear metaphors (e.g., 'shape of token you care about'). It lacks a clear structure and is more confusing than 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?

With an output schema present but not described, the description omits any detail about the return value. It mentions metrics loosely but does not specify what the response contains, leaving significant gaps for the agent.

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 schema already provides descriptions for 'filters' and 'window_days'. The tool description adds little, only restating the {min,max} format and default value without explaining what these filters mean or how they affect results.

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 mentions 'Research, not screening' and lists metrics like 'collapse rate' and 'peak-gain percentiles', but it does not clearly state what action the tool performs. The phrase 'Describe the shape of token you care about' is ambiguous, leaving the core function unclear.

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 only a vague contrast ('Research, not screening') and does not explicitly state when to use this tool over siblings. No conditions or scenarios are given to guide selection among the many listed tools.

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