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search_salaries

Read-only

Query general salary data by job title and geographic location. Returns average salary, salary range, number of data points, and median compensation. Use for career planning, negotiation benchmarking, or compensation analysis across roles and regions.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
locationNoGeographic location for salary lookup (e.g. 'San Francisco, CA', 'remote', 'United States')
job_titleYesJob position or role (e.g. 'Senior Software Engineer', 'UX Designer', 'DevOps Engineer')

TDQS

A4.2/5.0
Behavior4/5

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

Annotations provide readOnlyHint and openWorldHint; description adds detail on return data (average, range, count, median). No mention of rate limits or data freshness, but overall good transparency.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Two concise sentences front-loaded with purpose, followed by return details and use cases. No wasted words.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given simplicity (2 params, no output schema, good annotations), description is adequately complete. Covers purpose, output, and use cases. Missing data source info but not critical for a query tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% (both parameters have descriptions). Description does not add new parameter semantics beyond schema, so baseline score of 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

Clearly states it queries salary data by job title and location, distinguishes from sibling search_h1b_salaries by specifying 'general salary data', and lists specific return fields.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly mentions use cases (career planning, negotiation benchmarking, compensation analysis) and implies when to use vs sibling via 'general' vs H1B-specific, but lacks explicit exclusion or alternative naming.

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

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TDQS

A4.1/5.0
Disambiguation5/5

The two tools have clearly distinct purposes: one focuses on H1B visa salary data, the other on general salary data. Descriptions make the difference explicit, so an agent can easily select the correct tool.

Naming Consistency5/5

Both tools follow a consistent 'search_' prefix pattern with clear, descriptive nouns (h1b_salaries, salaries). The naming convention is uniform and predictable.

Tool Count3/5

With only 2 tools for a HR & compensation domain, the set feels thin. While each tool serves a specific purpose, additional tools (e.g., for benefits, cost-of-living, or industry breakdowns) would be expected for comprehensive coverage.

Completeness3/5

The tools cover general and visa salary data, but lack other common compensation-related queries such as experience-level filtering, benefits information, or historical trends. This creates notable gaps for thorough analysis.

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