Systemonomic

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

With no annotations provided, the description carries full burden for behavioral disclosure. It mentions using an LLM and being 'more sophisticated' but lacks critical details: whether this is a read-only analysis or creates data, what permissions are needed, potential costs/rate limits of LLM calls, or what 'WDA objects' are. The description doesn't adequately cover behavioral traits for an AI-powered tool.

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 efficiently structured with a clear purpose statement first, followed by comparison to sibling tool, then parameter explanations. Every sentence adds value with no wasted words. The two-sentence format with bullet-point parameter explanations is appropriately concise.

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 has an output schema (which handles return values), 2 parameters with 0% schema coverage, no annotations, and moderate complexity (AI-powered analysis), the description is partially complete. It covers the basic purpose and parameters but lacks important context about what WDA objects are, how the LLM analysis works, and behavioral aspects. The output schema existence helps, but more operational context is needed.

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. It provides basic semantics for both parameters: 'project_id' identifies the project to analyze, and 'provider' specifies the LLM provider with default and options. However, it doesn't explain what format project_id should be, what WDA objects are, or provide examples. The description adds some value but doesn't fully compensate for the schema coverage gap.

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: using AI to derive detailed task suggestions from WDA objects. It specifies the verb 'derive' and resource 'task suggestions', and distinguishes from sibling 'generate_tasks_from_wda' by noting it's 'more sophisticated' and uses an LLM. However, it doesn't fully explain what makes it more sophisticated beyond LLM usage.

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 clear context by explicitly comparing to sibling 'generate_tasks_from_wda' and stating this is more sophisticated. It implies when to use this vs. the simpler alternative. However, it doesn't mention other potential alternatives like 'run_atss_batch' or provide explicit when-not-to-use guidance.

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