HALOWERK: Check a model answer against a JSON schema and get every deviation named in plai

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

Beyond the read-only/no-destructive annotations, the description discloses paid usage via x402 (0.002 USDC per call), support for multiple JSON Schema drafts, separate reporting of wrapping vs schema errors, and optional repair suggestions. This adds significant behavioral context not present in 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 front-loaded with a strong purpose statement, but pricing information is repeated twice, once in English and twice in German, adding redundancy. The extra German paragraph does not earn its place, though the rest is well organized.

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 description covers input modes, unwrapping behavior, schema draft support, repair options, limitations, and return contents (paths, rules, wrapping). With no output schema, it gives a reasonable but not exhaustive picture of the response shape, leaving some detail to the agent's expectation.

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 100%, giving baseline 3. The description adds value by explaining the output/output_text mutual exclusivity and that wrapping is reported separately, which clarifies the combined behavior beyond individual parameter descriptions. It also specifies that suggest_repairs covers defaultable missing fields and extra fields.

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 opening sentence clearly states a specific action: checking a model answer against a JSON schema, reporting deviations in plain language with paths, and noting wrapping. This distinguishes it from sibling tools like tool_arg_validate by focusing on model outputs and wrapping detection.

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 provides clear usage context: it accepts parsed or raw model output, handles code fences/prose, and explicitly limits itself to structure ('never truth'), telling users not to use it for factual verification. It does not name alternative tools, but the boundary is clear.

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