search_metric_categories

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and openWorldHint=false, so the safety profile is fully covered. The description adds only that output is a Markdown table of categories, counts, and descriptions, which is largely restated by the output schema. No contradictions, but little behavioral value beyond structured fields.

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?

Front-loaded purpose, then a one-line usage instruction, then a short Returns block. The Returns line is somewhat redundant given a declared output schema, but overall it is tight and readable.

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?

An output schema exists, so return-value detail is not required, and the description still confirms the shape. For a zero-parameter discovery entry point, an agent has everything needed to call it and act on the result.

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 tool takes zero parameters, so the baseline is 4. The description correctly implies no input is needed, and there are no parameters left ambiguous.

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 and resource: list the categories of what the server calculates, enumerating them (prices, financial statements, ratios, models, risk, economics) and noting it also returns per-category tool counts. This clearly distinguishes it from the many sibling tools that are themselves category names (volatility, momentum, rates) and from search_by_category/search_metrics.

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?

Gives explicit ordering guidance ('Use this first to understand what is available') and names the exact follow-up tool and its argument ('then call search_by_category with a specific category name'). No inference required about when to reach for this versus a category tool.

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