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

With no annotations, the description carries the full burden. It discloses the analytical depth ('three layers deep'), which is useful behavioral context, but says nothing about whether this is a pure reasoning pass, whether the input is treated as read-only, or how exhaustively the mapping is explored.

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?

A single front-loaded sentence with zero filler; the verb and depth constraint both land immediately. It may be slightly too terse for a framework in a crowded sibling set, but every word earns its place.

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?

For a one-parameter reasoning tool with no output schema, the description is minimally adequate but leaves open what the returned mapping looks like (layers, branches, tradeoffs) and when a caller should prefer another framework. An agent can invoke it, but not confidently compare it to alternatives.

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 single parameter's schema description already enumerates acceptable inputs (problem, decision, project, question). The description adds only the implication that a decision is the preferred input, which is marginal beyond the schema.

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 ('Map') and resource ('consequences of a decision') plus scope ('three layers deep'), which lets an agent distinguish it from a simple pros/cons list. However, it does not explicitly contrast with the many sibling frameworks (pre_mortem, inversion, scenario_planning) that also project downstream effects, so sibling differentiation is left to inference.

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?

Usage is only implied: a decision with non-obvious downstream effects is the natural trigger. There is no explicit when-to-use, when-not-to-use, or routing hint relative to the 18 sibling frameworks, so the agent must guess the boundary conditions.

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