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

Annotations already mark this as read-only and non-destructive, lowering the bar. The description adds valuable context: the data source ('Stratalize SIA 2024-style composite'), example output values, and the note about specialty premiums. It does not contradict annotations and provides more than just a safe-read indication.

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 use case, then states the return value, provides a concrete example, and cites the source. Every sentence serves a purpose, and the example is illustrative without being verbose.

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?

There is no output schema, but the description explains the core return (median markup, low/high band) and gives example values. It also covers source and use context. Minor gap: it doesn't describe the exact response structure or edge cases (e.g., what happens if no filters are provided), but it is sufficient for a read-only analytics tool with simple parameters.

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. It names both parameters indirectly ('by agency and specialty type') and gives concrete examples (AMN Healthcare, ICU), making it clear these are filters. However, it doesn't specify whether parameters are optional or how to format values, leaving some ambiguity.

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 returns median markup percentage with low/high bands by agency and specialty type, with a specific use case (evaluating staffing agency pricing or negotiating travel nurse/locum contracts). It distinguishes itself from siblings like get_travel_nurse_rate_benchmark by focusing on markup rather than rates.

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 opens with 'Use when evaluating staffing agency pricing or negotiating a travel nurse or locum contract,' providing explicit context for when to invoke the tool. It does not explicitly mention when not to use it or name alternative tools, but the use case is clearly differentiated from sibling tools.

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