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hr-compensation-mcp-server

search_h1b_salaries

Read-only

Search the U.S. H1B visa salary database for sponsored employment data. Returns employer name, job title, approved salary, visa year, work location (city/state), and visa status. Use for understanding visa compensation trends, benchmarking tech salaries, or researching employer sponsorship patterns.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
companyNoCompany name or partial name (e.g. 'Google', 'Meta', 'Apple')
locationNoWork location as city or state (e.g. 'San Francisco, CA', 'Seattle, WA', 'New York')
job_titleNoJob title to search (e.g. 'Software Engineer', 'Data Scientist', 'Product Manager')

TDQS

A4.2/5.0
Behavior4/5

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

Annotations already establish readOnlyHint=true, and the description adds a valuable list of return fields (employer name, job title, approved salary, visa year, location, visa status) which is especially helpful because no output schema exists. It does not disclose details like pagination or result limits, but the added context goes beyond the annotations.

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?

The description is only two sentences, front-loaded with the action and resource, and every sentence contributes value. It avoids redundant details already present in the schema or annotations.

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?

With no output schema, the description compensates by enumerating the return fields. It also suggests use cases, which adds context for the agent. However, it omits details about query combination logic, result ordering, or potential limits, which could be important for a search tool, keeping it just below complete.

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?

All three parameters (company, location, job_title) are fully described in the input schema with examples, resulting in 100% schema coverage. The description does not add extra parameter semantics, such as how filters combine or whether partial matches are allowed, so the 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?

The description uses a specific verb 'Search' and identifies a clear resource ('U.S. H1B visa salary database'). It distinguishes itself from the sibling tool 'search_salaries' by explicitly focusing on H1B sponsored employment data, making the purpose unambiguous.

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?

It provides clear use cases such as 'understanding visa compensation trends, benchmarking tech salaries, or researching employer sponsorship patterns', which tell the agent when to use it. However, it does not explicitly mention the alternative tool or provide exclusion criteria, so it falls short of a 5.

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

A4.2/5.0
Disambiguation4/5

The two tools are clearly distinguishable: one targets H1B-specific salary data, the other general salary by job title/location. Descriptions explicitly clarify their intended use cases, though both are searches for salary information so there is minor potential for confusion.

Naming Consistency5/5

Both tools follow the same `search_` prefix with a descriptive noun: `search_h1b_salaries` and `search_salaries`. This is a consistent, predictable verb_noun pattern.

Tool Count3/5

With only 2 tools, the server feels thin for a compensation data domain. The scope is narrow, but the count is borderline and not egregious; it could be expanded with additional query types or detail lookups.

Completeness4/5

The server covers both H1B-specific and general salary searches, which are the primary use cases for compensation data. Minor gaps exist (e.g., no direct comparison tool or lookups by specific employer ID), but agents can likely achieve most goals with these two tools.

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