get_average_salary

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

No annotations are provided, so the description carries the full behavioral burden. It adds useful context by naming the data sources (CAGED + Glassdoor) and year (2026), and it discloses the return values (minimum, median, maximum). However, it omits response format, error behavior for unknown professions, and whether the data is fetched live, leaving transparency partial.

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?

Two short sentences front-load the core function and return metrics, then add data-source context in the second sentence. There is no redundant wording; every phrase earns its place.

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 one-parameter lookup with no output schema, the description covers the essential return values (minimum, median, maximum) and data provenance. It is nearly complete, though it does not state limitations such as profession coverage breadth or whether the data is refreshed periodically.

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 fully documents the single required parameter 'profession' with a description and examples, so schema coverage is 100%. The tool description adds no additional parameter guidance such as case sensitivity or accepted name formats, keeping it at the baseline score for fully covered schemas.

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 uses a specific verb ('Retorna') and resource ('salário médio de uma profissão no Brasil') and lists the exact return metrics (mínimo, mediana, máximo). It clearly distinguishes this lookup tool from sibling calculators like calculate_net_salary or calculate_thirteenth_salary by stating it returns data rather than computes a value.

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?

Usage is implied: call this tool when you need average salary statistics for a Brazilian profession. However, it does not explicitly state when to prefer it over alternatives such as get_cost_of_living or calculate_net_salary, nor does it mention exclusions or prerequisites.

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