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Execute statistical regression testing for LLM agents to identify actual behavior changes, reporting p-value, effect size, and confidence interval from CLI output.

Instructions

Run the agent-regress CLI with the given arguments and return its --json output, parsed. Real CLI --help output:

Run the agent-regress CLI (statistical regression testing for LLM agents).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
argsYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior2/5

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

With no annotations, the description must carry the full burden of behavioral disclosure. It mentions that the tool returns parsed --json output, which is useful, but it does not disclose potential side effects of running arbitrary CLI arguments, such as file modifications or system changes. For a generic runner, this lack of safety information is a significant gap.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is short and front-loaded with the main action. However, the quoted 'Real CLI --help output' repeats the same purpose statement, making it slightly redundant. Overall, it is efficient and well-structured, though a single sentence would have sufficed.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given that this is a simple wrapper with one parameter and an output schema, the description is reasonably complete for basic use. However, it does not provide examples of typical arguments or explain error handling/exit codes, which would be valuable for a CLI runner. The output schema likely covers return values, but the description could still offer more context.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has one parameter 'args' with 0% description coverage, so the description must compensate. It says 'with the given arguments,' which clarifies that the array items are CLI arguments, but it does not elaborate on format, ordering, or examples. This adds minimal meaning beyond the schema but is not completely unhelpful.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool runs the agent-regress CLI with given arguments and returns parsed JSON output. It is specific about the resource (agent-regress CLI) and the action (run), and mentions statistical regression testing for LLM agents, leaving no ambiguity about its purpose.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides clear context by identifying the tool as a regression testing utility for LLM agents, implying when it should be used. Although there are no sibling tools to compare against, the context is specific enough to guide an agent. It doesn't explicitly exclude alternatives but does not need to, as no alternatives exist.

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

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