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.

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

With no annotations, the description carries the full burden for behavioral disclosure. It states that the tool returns a list with one-paragraph descriptions, which is a behavioral outcome. However, it does not explicitly state that it is read-only, non-destructive, or what happens if there are no rules. For a simple list operation, this is adequate but not enriched—no mention of side effects, ordering, or potential errors.

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 sentence, front-loaded with the action and purpose. It conveys the exact scope ('every rule') and the output format ('one-paragraph description'). There is zero waste; every word earns its place.

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 simple list tool with no parameters and an output schema, the description is sufficient to convey what the agent will get. It covers the full scope ('every rule') and the format of each entry. The main missing element is an explicit statement about its read-only nature, but given the simplicity and the existence of an output schema, the description is adequately complete.

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?

The tool has zero parameters, and the schema already reflects that (coverage 100%). The description adds no parameter info because none are needed. Per the rubric, 0 params gives a baseline of 4, and the description does not need to compensate for any missing parameter documentation. There is nothing to clarify.

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 action 'List every rule' with a specific resource ('the scanner applies') and the nature of the content ('with a one-paragraph description of each'). It distinguishes itself from siblings: explain_rule presumably explains a single rule, and scan_tests likely performs scanning. The verb 'list' and scope 'every' make the purpose unambiguous.

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 no guidance on when to use this tool versus the siblings (explain_rule, scan_tests). It does not mention any conditions, prerequisites, or exclusions that would help an agent decide. The agent must infer from the name and the minimal description that this is for getting an overview, but no explicit direction is given.

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

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 does so effectively: it explains what the tool detects, how detection differs across languages (exact vs. heuristic), and that every finding includes file and line. It also discloses that include_skipped findings are reported at 'info' severity. The one gap is that it does not explicitly state the tool is read-only (no file modifications), though 'scan' strongly implies it. Overall, it is far more transparent than typical tool descriptions.

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 well-structured and front-loaded: the core purpose is stated in the first sentence, followed by detailed detection rules, then language-specific behavior, and finally parameter details. Each paragraph adds distinct value without repetition or fluff. The format is scannable and efficient for an agent.

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?

Given the presence of an output schema, the description need not enumerate return fields, but it still provides rich context: what the tool detects, severity semantics, language-specific accuracy, and parameter behavior. An agent has all necessary information to invoke the tool correctly, choose appropriate inputs, and interpret results. It is complete for a scanning tool of this complexity.

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%, so the description must compensate, and it does. The 'Args' section fully explains each parameter: path (file/dir, auto-skipped dirs), include_skipped (why include, severity implications), and max_findings (cap behavior, summary counts all). This goes beyond the schema by providing behavioral context, making parameter semantics extremely clear.

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 opens with a specific verb and resource: 'Scan a file or directory for tests that cannot fail.' It then enumerates concrete categories of such tests (true-by-construction assertions, no assertions, empty bodies, skipped). The purpose is unambiguous and distinct from sibling tools list_rules and explain_rule, which operate on rules rather than tests.

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 clarifies when to use the tool: it is for scanning files/directories for non-failing tests, and it explicitly notes that build/dependency directories are auto-skipped. It also distinguishes Python analysis (exact via ast) from Rust/JS (heuristic), helping agents set expectations. While it does not name alternative tools for comparison (the siblings are clearly rule-focused, not test scanners), the use case is sufficiently well-scoped. No explicit when-not-to-use guidance, but this is minor given the clear domain.

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