Sibyl

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 mentions using an LLM for analysis, which hints at AI-based processing, but lacks details on permissions, rate limits, response format, or potential side effects. For a tool with no annotations, this leaves significant gaps in understanding how it behaves.

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 front-loaded with a clear purpose statement, followed by a structured 'Args' section. Every sentence earns its place by providing essential information without redundancy. It's appropriately sized for a tool with 2 parameters and no complex annotations.

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 tool's complexity (2 parameters, no annotations, but has an output schema), the description is minimally adequate. It covers the purpose and parameters but lacks behavioral details and usage guidelines. The output schema exists, so the description doesn't need to explain return values, but overall completeness is limited to basic functionality.

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 description includes an 'Args' section that explains both parameters: 'text' as 'The text content to analyze' and 'question' as 'What to analyze about the text'. This adds meaningful semantics beyond the schema, which has 0% description coverage and only provides titles. Since there are 2 parameters and the description compensates well, a score of 4 is appropriate.

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 purpose: 'Analyze provided text with a specific question using LLM.' This specifies the verb ('analyze'), resource ('text'), and method ('using LLM'), making it easy to understand. However, it doesn't explicitly distinguish this tool from sibling tools like 'research' or 'swot', which might also involve analysis, so it doesn't reach the highest 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?

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention sibling tools like 'research' or 'compare', nor does it specify prerequisites or exclusions. The only implied context is analyzing text with a question, but this is too vague for effective tool selection.

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