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

With no annotations, the description carries the full burden, and it delivers useful behavior: the return payload (chosen option, confidence, per-option probabilities, 'uncertain') and the escalation policy when uncertain. It does not disclose that this is a model-driven, potentially non-deterministic call whose confidence threshold is user-tunable.

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?

Three sentences, front-loaded with the action and its scope, then examples, then return shape, then the escalation rule. No sentence is filler and nothing is buried.

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?

With no annotations and no output schema, the description compensates well by describing the return payload and the uncertain-handling path, which are the two things an agent most needs. The remaining gap is the absence of any guidance on choosing this tool over its siblings for a given decision.

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 all four parameters are already documented in the schema, including min_confidence's threshold semantics; baseline 3 applies. The description only indirectly reinforces 'context' via the example questions and adds what happens downstream of the uncertain flag rather than new per-parameter meaning.

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?

States a specific verb and resource: 'Decide one multiple-choice question for the given context', backed by two concrete examples ('How big is this work?', 'What is the tone of this comment?'). The 'one multiple-choice question' framing also implicitly separates it from the binary ask_yes_no sibling and from the rule-evaluation siblings.

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?

Gives clear operating context plus an explicit conditional rule: when the 'uncertain' flag is true, ask the user instead of acting. It does not, however, say when to prefer this tool over ask_yes_no or evaluate_rules, so no true when-not/alternative routing is provided.

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, and it does disclose the return contract (decision, probability of yes, 'uncertain') plus the escalation semantics. It does not describe determinism, latency, or whether the call has side effects, but the key behavioral trait — defer to the user on uncertainty — is explicit.

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?

Three tight sentences: purpose with examples, return values, then the escalation rule. Front-loaded and free of filler; each sentence 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?

There is no output schema, so the description correctly compensates by naming the three returned fields. With threshold and margin fully documented in the schema, the only remaining gap is guidance on choosing threshold/margin values, which is minor.

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 every parameter including threshold and margin is already documented in the schema with its meaning and bounds. The description adds no syntax or interpretation beyond what the schema provides, so the baseline 3 applies.

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?

States a specific verb and resource ('Decide one yes/no question') and immediately grounds it with two concrete examples drawn from plausible agent contexts. The yes/no framing implicitly distinguishes it from the ask_choice sibling without needing to name 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?

Gives a clear conditional rule: 'When uncertain is true, ask the user instead of acting.' This is exactly the guidance an agent needs to avoid acting on a low-confidence decision. It stops short of naming when to prefer this tool over ask_choice or evaluate_rules.

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 and does meaningfully: it discloses the result shape for both rule types and gives an actionable behavioral rule ('When uncertain is true, ask the user instead of acting'). It does not cover error behavior or how the single Jev call handles large rule sets, but the uncertainty-handling guidance is genuine behavioral value.

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?

Three sentences, each load-bearing: scope, output shape (needed because there is no output schema), and the uncertainty-handling rule. The scope is front-loaded and nothing is repeated from the schema.

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 tool with no output schema and no annotations, the description usefully explains both result variants and the uncertain flag. The nested custom_rules structure and file-resolution defaults are left entirely to the schema, which is acceptable given 100% coverage, but a note on rule precedence would round it out.

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 only a loose mapping of 'rules from the rules file and/or custom rules' to the rules_file/custom_rules parameters, with no format, precedence, or override semantics beyond what the schema already documents.

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 ('Decide several rules at once ... for the given context') and the phrase 'several rules at once' implicitly contrasts with the single-decision siblings ask_yes_no and ask_choice. It stops short of naming those siblings explicitly, so differentiation is inferable rather than stated.

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?

'Decide several rules at once' implies the batch use case, and 'Uses rules from the rules file and/or custom rules' hints at the input source, but there is no explicit when-to-use versus ask_yes_no/ask_choice or list_rules, and no exclusions. Usage is left to inference.

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?

No annotations exist, so the description carries the burden. 'Show' implies a non-destructive read, which is the key behavioral trait for this tool, and it hints at the returned content (rule_ids). But it says nothing about behavior when the file is missing, empty, or when the env-var default fails to resolve.

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?

One sentence, zero waste, with the action front-loaded and the motivation trailing. Nothing to trim and nothing missing for the size.

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 zero-required-parameter read-only listing tool with no output schema, the description supplies the action, the target file, and the reason to call it. Return shape is only loosely hinted ('rule_ids'), which is a minor gap given no output schema exists.

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% for the single rules_file parameter, including the env-var and working-directory default chain, so the baseline is 3. The description adds no format or resolution details beyond what the schema already documents.

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?

States a specific verb and resource ('Show the rules defined in the rules file') and adds the agent-facing purpose of discovering valid rule_ids. It does not explicitly differentiate itself from the sibling evaluate_rules, though the discover-vs-evaluate distinction is implied.

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?

'so you know which rule_ids exist' implies the usage context: consult this before invoking evaluate_rules. However, no alternative is named and no when-not-to-use condition is given, so the guidance is implied rather than explicit.

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