ALCBar AI Practice Risk Check

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

Annotations cover the safety profile (readOnly, idempotent, non-destructive), so the description must add context instead, and it does: it discloses the content scope (six enumerated duties from Opinion 512) and an important behavioral limitation (state rules vary, so always direct the user to their own jurisdiction). It does not discuss length/format beyond "short, general summary".

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?

Front-loaded with the usage trigger, followed by scope and caveat in two tight sentences; every clause earns its place. Minor redundancy between the enumerated duty list and the enum values keeps it from a 5.

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 output schema, the description carries the return-value burden and does so: it says what the answer contains (a short general summary of six named duties) and the required caveat. For a single-optional-param, read-only advisory tool, nothing an agent needs is missing.

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% and the single parameter is an enum with its own description, so the baseline is 3. The description's enumeration of the six duties loosely maps to the enum values but adds no syntax, default, or combination behavior beyond what the schema already states.

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 (explain), resource (AI ethics duties), and audience (U.S. lawyers), and pins the content to ABA Formal Opinion 512 with a date. An agent can immediately tell this apart from the sibling risk-scoring tools, which measure rather than explain.

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?

"Use when a U.S. lawyer asks what ethics duties apply to using generative AI" gives a clear triggering condition, and the jurisdiction caveat bounds when it should not be treated as authoritative. It does not explicitly name alternatives or state when-not-to-use, 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.

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

Annotations already declare readOnlyHint, idempotentHint and non-destructive behavior, so safety is covered. The description still adds meaningful context beyond them: the tool returns exactly 12 questions, each with options a, b and c — useful given there is no output schema. It stops short of describing error handling or whether the question set is fixed/versioned, so not a 5.

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 short sentences, zero waste. The usage trigger comes first, the return shape second, and the required next action last, which matches the order an agent needs them.

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 output schema, the description carries the return-value burden and does so by stating the count (12) and answer format (options a, b, c). Combined with the follow-up instruction to score_ai_practice_risk, an agent has everything needed to invoke it correctly.

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 takes zero parameters, which is the baseline-4 case; the empty schema leaves nothing for the description to clarify or compensate for. No parameter confusion is possible.

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 — retrieves the 12 AI Practice Risk Check questions — and pins the audience (U.S. lawyer or law firm). It is clearly distinguishable from sibling score_ai_practice_risk because it returns questions rather than a risk score.

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 an explicit trigger condition ('when a U.S. lawyer or law firm wants to check how safely their firm uses generative AI') and names the exact follow-up tool with the sequencing required ('ask every question, then call score_ai_practice_risk'). An agent knows both when to call this and what to do next.

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?

Annotations already cover safety (readOnly, idempotent, non-destructive, closed-world). The description adds genuinely new behavioral context: 'Answers are not stored' discloses a privacy/data-retention trait, and it describes the exact output shape including instructions_for_assistant and a report link. It does not mention rate limits or error behavior, but the added value over annotations is real.

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?

Front-loaded with the core action and then the return contents; the closing presentation instruction is directive but earns its place by constraining agent behavior. Slightly dense in the output enumeration, but no sentence is wasted.

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 output schema, the description carries the full burden of describing return values and does so thoroughly (score out of 100, seven areas, three weakest areas with next steps, report link). Combined with the input-dependency note, an agent has everything needed to invoke and render the result correctly.

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% and the schema already documents the single answers array, its a/b/c enum, and the exactly-12 min/max constraint. The description only reinforces '12 answers' and the ordering, adding no format or syntax detail beyond the schema. Baseline 3 is correct.

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 (scores) and resource (the 12 AI Practice Risk Check answers), and explicitly ties the input to the sibling tool get_risk_check_questions. An agent can distinguish it from the sibling question-fetching tool without opening any schema.

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 establishes the workflow dependency (answers come from get_risk_check_questions) and prescribes how to present results (show only what the tool returns, follow instructions_for_assistant). It does not state explicit when-not conditions or alternatives, but for a terminal scoring step the context is clear.

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