aflpp.minimize_corpus

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions output as a new corpus directory, but it fails to disclose that the tool likely executes the target command (target_cmd) many times, which could have significant side effects. It also doesn't state whether the original corpus is left untouched or if any files are deleted.

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 a single concise sentence that conveys the core action and result. There is no fluff or redundant information. It earns its place by naming the method (afl-cmin) and the outcome (new corpus directory).

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?

The tool has 7 parameters, no output schema, and no annotations. The description is far too minimal to understand the tool's full requirements, side effects, or expected behavior. It does not explain what the input corpus must look like, how the target command is used, or what the returned output is.

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 description coverage is 0%, and the description provides no explanation of the 7 parameters (workspace, corpus_name, target_cmd, timeout_ms, etc.). Parameter names like 'target_cmd' and 'output_corpus_name' are somewhat self-explanatory, but the description adds no semantic meaning beyond the raw 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 uses a specific verb ('Minimize') and resource ('corpus'), and mentions the underlying tool (afl-cmin). It clearly distinguishes from sibling tools like 'minimize_testcase' (which minimizes a single testcase) and 'import_corpus' (which imports rather than minimizes).

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 gives clear context on what the tool does, but it does not explicitly state when to use it versus alternatives, nor mention prerequisites (e.g., corpus must already exist, workspace must be initialized). It only implies usage: you use this when you want to reduce a corpus.

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