Search H1B visa salary data for a job title. Returns employer, salary, location. Args: job_title: Job title (e.g. 'Data Scientist') location: City or state (optional)
| Name | Required | Description | Default |
|---|---|---|---|
| location | No | ||
| job_title | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The annotations already declare readOnlyHint=true, so the agent knows this is a safe read operation. The description adds the return fields (employer, salary, location) and the arg list, but does not disclose deeper behavioral traits like data source limitations, pagination, or precision. This is consistent with annotations, so no contradiction.
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?
The description is extremely concise: a one-sentence purpose, a one-line return summary, and a compact Args list. Every sentence earns its place, and the most important information is front-loaded.
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?
The tool is simple, has no output schema, and the description explains what it returns (employer, salary, location). It also specifies parameter optionality. Some minor gaps remain (e.g., result count, data source specifics), but given the low complexity and good annotations, this is nearly complete.
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 description coverage is 0%, so the description must compensate. It does: job_title is explained with an example ('Data Scientist') and location is described as 'City or state (optional)'. This adds meaning beyond the raw schema, though it could give more examples or format constraints.
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 searches H1B visa salary data with a specific verb ('Search') and resource ('H1B visa salary data'), and explicitly lists the return fields (employer, salary, location). This distinguishes it from sibling tools like company_reviews and search_jobs, which serve different purposes.
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 implies when to use the tool (when you need salary data for a job title) and provides examples for job_title and location. However, it does not explicitly mention when not to use it or name alternative tools, so it stops short of a full 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Each tool targets a distinct aspect of the job market: company reviews, salary data, and job listings. There is no overlap, so an agent can easily select the right tool based on the user's intent.
The names are readable but follow mixed conventions: 'company_reviews' and 'salary_search' are noun phrases, while 'search_jobs' is a verb_noun structure. This inconsistency is minor and does not hinder understanding, but it deviates from a consistent verb_noun pattern.
Three tools is a reasonable, focused set for a job market server covering core queries: reviews, salary, and job search. It feels appropriately scoped, though it is on the smaller side and could benefit from one or two additional utilities.
The tools cover the primary job market data sources: job listings, company reviews, and salary information. There are no obvious dead ends, though additional tools like company profile details or job application tracking would enhance completeness, but they are not essential.