explain_rule

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

No annotations are provided, so the description carries the full behavioral burden. It does disclose the core behavior (returns an explanation of detection and resolution), but says nothing about error handling for unknown rule ids, the structure of the returned explanation, or any side effects. Given an output schema exists, the return structure gap is partly covered, but the description alone is thin on behavioral detail.

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 purpose is front-loaded in the first sentence, and the argument documentation follows in a compact, readable block. Every sentence earns its place — the example and provenance note are exactly the information an agent needs. No filler, no redundancy.

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 single-parameter tool of low complexity with an output schema present, the description is largely sufficient: it states the tool's purpose, what the output covers, and where the argument value comes from. Minor gaps like behavior on invalid rule ids are acceptable given the simple scope and the output schema's presence.

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?

With 0% schema description coverage, the description fully compensates for the 'rule' parameter by stating its provenance ('as returned by list_rules or by a finding') and providing a concrete example ('self-referential-source'). This adds real meaning beyond the bare string type in the schema, though it stops short of enumerating valid values or format constraints.

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+resource pair ('Explain one rule') and spells out what the explanation covers ('what it detects and how the finding is usually resolved'). The purpose is unambiguous and clearly distinct from list_rules (which lists rules) and scan_tests (which runs scans), though it never names those siblings explicitly, so differentiation relies on the reader inferring it.

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 implies a workflow by telling the agent the rule id comes 'as returned by list_rules or by a finding', which subtly signals that explain_rule is the follow-up step to understanding a rule. However, it never explicitly states when to use this tool versus alternatives, nor any conditions where it should not be used. The guidance is implied rather than stated.

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