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

With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions the deterministic bisection algorithm, monotonicity assumption, and return values (required input, change from baseline, achieved metric, residual). It does not disclose potential error conditions (e.g., unreachable target), but the monotonicity assumption indicates a key limitation. Overall, this is solid transparency for a calculation 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 four sentences long, front-loaded with the primary purpose, and every sentence earns its place: purpose, required inputs, optional bounds, and return values/assumption. It is concise, scannable, and free of unnecessary filler.

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 tool with 8 parameters and no output schema, the description covers the core workflow (inputs, optional bounds, returns, assumption). It does not explain the 'inputs' or 'horizon' parameters, but the schema provides adequate detail for those. The return values are explicitly listed, compensating for the missing output schema. A brief note about what happens when the target is unattainable would be useful, but the description is largely complete.

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 input schema already provides detailed descriptions for all parameters (100% coverage), so the baseline is 3. The description clarifies the relationship between key parameters (template, solve_for, target_metric, target_value, bounds) and notes that target_metric defaults to the primary output, which is also in the schema. It adds no novel semantic information beyond what the schema already conveys, so a 3 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 function: 'Solve for the input value required to make an output metric hit a target value.' It uses a specific verb ('solve') and resource ('input value', 'output metric'), and it distinguishes itself from siblings like run_scenario and sensitivity_analysis by focusing on inverse root-finding rather than forward simulation or sensitivity exploration.

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 implicitly communicates when to use the tool by explaining the required inputs and optional bounds. It does not explicitly mention alternatives or exclusions, but the context of sibling tools (e.g., run_scenario for forward projections) makes the use case clear. A sentence stating 'use this when you need to find the input for a target output' would improve it, but the guidance is adequate.

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