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

Annotations already declare readOnlyHint=true and destructiveHint=false, so the agent knows it is a safe read operation. The description adds operational context (updated annually, $0.02 USDC per call) but does not disclose any further behavioral traits such as rate limits, response format, or data caveats. Given the strong annotation coverage, this 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 overly wordy with redundancy: 'capital allocation' appears twice, and the use-case sentences overlap. Phrases like 'The most cited public finance benchmark' are promotional rather than functional. While front-loaded with the use case, it could be trimmed to two sentences without loss of information. Adequate but not a model of conciseness.

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 simple read-only benchmark tool with no output schema, the description covers the key decision-making context: use cases, data source, update frequency, and cost. It does not describe the exact response structure, but the output of a WACC benchmark is likely a single value or a small dataset, and the tool's simplicity makes this less critical. Overall, the description is sufficient for an agent to invoke it correctly.

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 0%, and the description compensates by explicitly naming the two dimensions: 'by sector and market cap tier'. This tells the agent what each parameter represents. The enum values in the schema are self-explanatory (e.g., sector names, market cap tiers), so the description's high-level naming is sufficient. It does not describe value formats or defaults, but the enums cover valid inputs.

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 explicitly identifies the resource ('WACC benchmarks by sector and market cap tier') and the data source ('Damodaran annual dataset'). This distinguishes it from sibling benchmark tools like get_ma_multiples_benchmark or get_pe_return_benchmark, which serve different metrics. The use-case framing ('Use when valuing a business, setting hurdle rates...') further clarifies what it returns.

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 clearly states when to use the tool ('Use when valuing a business, setting hurdle rates, or benchmarking discount rates for M&A analysis or capital allocation'). It does not explicitly name alternatives or exclusion conditions, but the use cases are specific enough to guide an agent's decision. The context is strong, though not as explicit as a direct 'use X instead' statement.

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