Search Indeed for job listings. Returns titles, companies, salaries, descriptions. Args: query: Job title or keywords (e.g. 'software engineer') location: City or state (optional, e.g. 'New York') max_results: Max results (default 20)
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | ||
| location | No | ||
| max_results | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and openWorldHint=true, reducing the burden. The description adds value by specifying the return fields (titles, companies, salaries, descriptions) and mention of 'Indeed' as an external source. It does not cover pagination or rate limits, but the annotations cover safety, so the additional context justifies a solid score.
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, with a clear first sentence stating the purpose and a structured Args block listing parameters. Every sentence adds value, with no filler or redundancy. The format is easy to scan and understand.
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?
Despite being a simple tool with no output schema, the description covers purpose, parameters, and return values. The context signals show only 3 parameters and 1 required, which the description explains well. For such a tool, this is a complete and self-contained description.
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?
The schema has no descriptions for parameters (0% coverage), so the description carries the full burden. It provides meaningful examples and defaults: query as 'Job title or keywords (e.g. 'software engineer')', location as optional 'City or state', and max_results with default 20. This fully compensates for the schema's lack of detail.
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 'Search Indeed for job listings' with a specific verb and resource. It distinguishes from sibling tools by focusing on job listings, even though it also returns salaries, which is a secondary feature. The explicit return fields further clarify its purpose.
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 usage for searching jobs but does not explicitly state when to prefer this over the alternative tools (company_reviews, salary_search). The overlap in returning salaries could cause confusion, but the primary focus on job listings gives some implicit guidance. No explicit exclusions or alternatives are mentioned.
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.