Screen content before it enters agent context

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

With no annotations, the description carries the full burden and does real work: it discloses the decision outputs (pass | review | block | skip) and the threshold semantics that drive them. It is silent on cost, latency, model/version behavior, or determinism, which keeps it short of a 5 for an external judging service.

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?

Two dense sentences plus a documentation pointer; the safety purpose is front-loaded and the output contract follows immediately. The trailing 'Pattern: docs.typesafe.ai/cookbooks/llm_guardrails.' is a marginally useful reference rather than core content, so it is efficient but not perfectly trimmed.

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 4-parameter judgment tool with no output schema, the description supplies the essential agent-facing facts: what is judged, the decision vocabulary, and the threshold defaults are visible in the schema. It does not describe the shape of the numeric scores returned alongside the recommendation, a minor 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 100%, so the baseline is 3, but the description adds genuine meaning beyond the schema: it explains that 'purpose' is what enables the relevance judgment and the 'skip' action, and it frames block_at/review_at conceptually as the injection-probability cutoffs that produce block/review.

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 states a specific verb and resource ('Judge fetched or external text') and enumerates exactly the three judgments it produces: prompt-injection probability, substantive-content check, and task relevance. That is far more informative than a name restatement, but it never distinguishes itself from plausibly overlapping siblings such as jev_gate or jev_classify, which is what separates a 4 from a 5.

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 a clear situational trigger: screen external or fetched content 'before an agent reads it', and notes that passing a purpose unlocks the relevance judgment and the 'skip' action. It stops short of naming alternatives or stating when NOT to screen (e.g. trusted local text), so no explicit exclusion guidance.

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