get_salary_history

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

With no annotations provided, the description carries full burden and excels by disclosing critical behavioral traits: rate limits (Error: 'API Error 429'), authentication requirements ('API Error 401'), default values (months default 12), constraints (max ~24 months), and error conditions for invalid inputs. The 'How to analyze' section adds valuable guidance on interpreting results.

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?

Well-structured with clear sections (PURPOSE, Args, Returns, Example, How to analyze, Errors), but slightly verbose at ~200 words. Every section earns its place by adding value, though some redundancy exists between the initial description and PURPOSE section. Front-loading is effective with the core purpose stated immediately.

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?

Exceptionally complete given the complexity: no annotations but the description covers purpose, usage, all parameters, output format (with example), analysis methodology, and error conditions. The output schema exists, so the description appropriately focuses on interpretation rather than re-describing return structure. Nothing essential is missing for a trend analysis tool.

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?

With 0% schema description coverage, the description fully compensates by providing rich semantic context for all 5 parameters: country (ISO codes with supported list), keywords (role filtering examples), location (example), category (reference to get_categories), and months (default, max, and usage guidance for different values). This goes far beyond what the bare schema provides.

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 with a specific verb ('Get') and resource ('historical salary trends for matching jobs'), distinguishing it from siblings like get_salary_histogram (distribution) or search_jobs (job listings). The PURPOSE section reinforces this with concrete use cases like trend analysis and negotiations.

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 provides clear context for when to use this tool (analyzing salary trends over time) and includes practical examples like 'Are X salaries going up or down?' However, it doesn't explicitly contrast when to use this versus alternatives like get_salary_histogram or get_top_companies, missing explicit sibling differentiation.

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