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

With no annotations provided, the description carries the full burden of behavioral disclosure. It reveals that the policy is compiled into SMT-LIB logic enforced by a mathematical solver, that it is deterministic ('The solver either proves an action complies or it doesn't'), and that it costs 300 credits and requires an api_key. It also states the return value (policy_id and test scenarios). This is substantial behavioral context, though it does not mention potential timeouts, rate limits, or failure modes.

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 four sentences, front-loaded with the core purpose. Every sentence adds value: purpose, mechanism, contrast with alternatives, and output/cost/requirement. There is no redundancy or fluff, making it highly concise and well-structured.

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?

Even though there is no output schema and no annotations, the description covers the essential context: what the tool does, how it works, what it returns, and the cost. It explains the return value (policy_id and test scenarios) and the deterministic nature. It does not specify whether the operation is synchronous or asynchronous, which could be relevant given the sibling poll_session, but overall it is sufficiently complete for a rule-creation tool.

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 100%, so the baseline is 3. The description adds minimal parameter-specific detail: it repeats the need for an api_key and gives a general instruction ('Write rules the way you would explain them to a colleague'), but the schema already explains the 'policy' parameter with examples and limits. Thus, the description only marginally enhances parameter understanding.

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 clearly states the tool's function: 'Turn a plain-English policy into a jailbreak-proof guardrail.' It uses a specific verb ('turn') and identifies the resource (policy) and the outcome (guardrail). It distinguishes itself from sibling tools like check_action and check_logic, which are for evaluating actions rather than creating rules. The mention of 'Returns a policy_id and test scenarios' further clarifies the purpose.

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 context for when to use the tool: when you have a plain-English policy and need to enforce it as a formal guardrail. It contrasts with typical LLM-based approaches by stating 'No prompt engineering. No LLM judges,' implying it is for deterministic enforcement. However, it does not explicitly name alternative tools or state when not to use it, so it falls short of a 5.

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