detect_fallacies

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It adds some context: it specifies that detection includes 'severity and correction' and mentions cost and service details. However, it lacks critical behavioral traits such as rate limits, error handling, or output format (since no output schema exists). The description does not contradict annotations, but it is incomplete for a tool with no structured behavioral data.

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 appropriately sized and front-loaded: the first sentence states the core purpose, and additional details (cost and service) are concise. However, the cost and service information, while potentially useful, could be considered extraneous if not directly relevant to tool selection. Overall, it is efficient with minimal waste.

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 complexity (a detection tool with behavioral nuances), lack of annotations, and no output schema, the description is incomplete. It covers the basic purpose and adds some cost/service context but misses key details like output structure, error conditions, or usage constraints. For a tool with no structured support, more comprehensive guidance is needed to aid an AI agent effectively.

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 for undocumented parameters. It does not add any meaning beyond the input schema—it does not explain what the 'argument' parameter should contain (e.g., text format, length limits) or provide examples. With one parameter and no schema descriptions, this leaves the parameter semantics largely undefined, failing to compensate adequately.

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: 'Detect logical fallacies in an argument with severity and correction.' It specifies the verb ('detect'), resource ('logical fallacies'), and scope ('in an argument'), but does not explicitly differentiate from siblings like 'verify_claim' or 'deep_verify_claim', which might involve similar argument analysis. This makes it clear but not fully sibling-distinctive.

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 alternatives. It mentions cost and service details ('Cost: $0.003 USDC. Service: debateclub.'), but these are not usage guidelines—they do not indicate scenarios, prerequisites, or comparisons with sibling tools like 'verify_claim' or 'analyze_call'. Without such context, users must infer usage based on the purpose alone.

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