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

Annotations already indicate a read-only, non-destructive operation. The description adds meaningful behavioral context by detailing the return values (median monthly spend, category breakdown, productivity medians) and providing a sample output, plus the source. This goes beyond the annotations without contradicting them.

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 well-structured with a use case, output summary, example, and source. It is somewhat verbose but every sentence adds value, and the example is particularly helpful for understanding the tool's output.

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?

Despite no output schema, the description provides sufficient context for a simple lookup tool: when to use it, what it returns, a realistic example, and the source. The main gap is the lack of parameter details, but this is already captured in parameter semantics, so overall completeness is solid.

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 0%, so the description must compensate. While it mentions that results are 'by industry' and gives a healthcare example, it does not explain the 'industry' parameter format or the optional 'company_size' parameter at all. The description offers minimal semantic value beyond what the parameter names imply.

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 purpose: validating IT spend against industry peers and building budget baselines. It specifies the output (median monthly spend, category breakdown, productivity medians) and uses a concrete example, making its function unambiguous and distinct from sibling tools focused on categories or company size.

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 provides two usage scenarios: validating total IT spend and creating budget baselines for board presentations. However, it does not distinguish when to use this tool over closely related siblings like get_category_spend_benchmark or get_industry_spend_profile, so it offers clear context without exclusions or alternative guidance.

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