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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false. The description adds extra behavioral detail beyond that: the lookup is deterministic, requires no network or auth, and produces an unadjusted or adjusted rate rather than a verdict or business case. It also clarifies the relationship of external research to this tool, which helps the agent reason about naming and expectations.

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 the core purpose and explicit call signal, then moves from parameter semantics to output clarification, alternatives, and technical behavior. Some detail is present, but every sentence contributes either a necessary constraint, a clarification, or an alternative. The structure is efficient for agent parsing.

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 two enum-constrained required parameters, an output schema, and overlapping sibling tools, the description is complete. It tells the agent when to use the tool, what the output is not, how to understand the function and industry parameters, and what behavior to expect. Nothing material is missing for correct invocation.

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 documents both parameters at 100% coverage with enum values and descriptions. The description restates the function/industry relationship without materially adding new semantics: function selects rate ranges and drivers, industry applies a multiplier, and universal returns the unadjusted base. This is helpful reinforcement, but the schema is doing the heavy lifting, so a mid-range score 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 opens with a specific verb and resource: 'look up the disclosed AI BVF planning rates... for one business function and industry.' It clearly defines what the tool returns — a rate as a fraction of revenue — and distinguishes it from scoring and portfolio tools by stating what it is not. This makes the tool's purpose easy to separate from all sibling tools.

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 explicitly says 'CALL THIS when the user wants to inspect revenue-uplift and cost-takeout assumptions before scoring, or to compare value drivers across functions.' It also gives direct alternatives: use score_initiative for verdicts, score_portfolio for multiple initiatives, and diagnose_process for measured operational waste. This is far beyond the threshold of minimal viability.

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