what_if_analysis

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

With no annotations provided, the description carries the full burden of disclosing behavior. It describes generating alternative approaches and scoring, implying a read-only analysis. However, it does not explicitly state whether it modifies any state or requires special permissions. The behavioral disclosure is adequate but not exceptional.

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 concise and front-loaded with the main purpose. It uses bullet points effectively to list the three approaches with conditions. Every sentence adds value, though it could be slightly more structured (e.g., using sections). Overall, it is well-organized for an LLM to parse.

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 that an output schema exists (context: has_output_schema: true), the description does not need to detail return values. It describes the output as 'multi-approach comparison and recommendation' and lists the approaches. This is sufficiently complete for an agent to understand what to expect, though it omits details on how scoring works.

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 baseline is 3. The description does not add additional meaning beyond what the schema already provides for the parameters task_id and team_id. It does not elaborate on optionality or formatting, so no extra value is added.

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 purpose: 'Perform What-If analysis on a task — generate multi-approach comparison and recommendation.' It uses a specific verb and resource, and the output is explicitly described. This distinguishes it from sibling tools like task_compare or task_decompose which serve different purposes.

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 usage context: 'During task planning' and details conditions for when each approach appears (e.g., idle agents >= 2 for parallel split, team has memory for history-driven). While it does not explicitly mention when not to use, it gives enough contextual cues for appropriate selection among many siblings.

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