analyze_salary_offer

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

Annotations already declare readOnlyHint, idempotentHint, openWorldHint, and destructiveHint=false, so safety is covered. The description adds genuine context by disclosing that analysis is AI-powered, relies on real market data, and involves the "client's LLM" — hinting at an external model dependency, but it says nothing about latency, cost, or what the analysis actually produces.

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?

A single short sentence with no padding, so it is concise and front-loaded. The cost is that the brevity comes from buzzwords ("AI-powered") rather than from conveying more useful information.

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 values need not be explained, and there is only one parameter. However, given the crowded sibling set of salary and job-analysis tools, the definition is not complete enough to let an agent confidently route to it, and the offer-vs-job ambiguity remains unresolved.

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 coverage is 100% and the single job_id parameter is fully documented in the schema with an example format, so the schema does the heavy lifting. The description adds no additional meaning about the parameter, which matches the baseline of 3 for high-coverage 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 names a verb and resource ("salary analysis") but is vague about what is actually analyzed — the tool name implies evaluating a candidate's offer, while the only parameter is a job_id, leaving the actual scope ambiguous. It also fails to distinguish itself from the sibling get_job_salary, which by name appears to deliver overlapping information.

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?

There is no when-to-use guidance at all: nothing says when an agent should call analyze_salary_offer instead of get_job_salary, get_job_trends, or analyze_job_viability. The agent must infer the choice from the name alone.

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