[Job Market] 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?
The readOnlyHint and openWorldHint annotations already signal safe read and variable results. The description adds return fields but does not disclose rate limits, pagination, or data freshness limitations. Contributes moderate additional context.
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 concise with a clear statement of purpose, return fields, and parameter explanations. No unnecessary words, well-structured.
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 description covers parameters and return fields adequately. However, it could mention potential limitations (e.g., Indeed-specific restrictions, output format). Still fairly complete for a simple search tool without output schema.
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 0%, so the description compensates by explaining each parameter with examples and defaults: query (required with example 'software engineer'), location (optional with example 'New York'), max_results (default 20). This provides meaning beyond the raw schema.
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 it searches Indeed for job listings and lists the returned fields (titles, companies, salaries, descriptions). This distinguishes it from sibling tools like company_reviews and salary_search.
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 use for job searches but does not explicitly state when to use this tool versus alternatives like salary_search or company_reviews. No exclusions or when-not guidance provided.
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.
Many tools have overlapping functionality across categories (e.g., multiple search_arxiv, search_google_scholar, real estate tools, DNS/WHOIS checks). An agent would struggle to differentiate between similar tools from different categories, leading to ambiguity.
Tools follow a 'category__verb_noun' pattern mostly, but verbs vary (get, search, screen, check, etc.) and some categories use different orders (e.g., 'get_repo_stats' vs 'search_repos'). The consistency is acceptable but not uniform across the entire set.
With 152 tools, the server is excessively large for a single MCP server. While it aims to be an all-in-one gateway, the sheer number overwhelms the agent and likely exceeds practical limits for coherent selection.
The server covers a wide range of domains (finance, real estate, news, developer tools, etc.) but has notable gaps (e.g., social media APIs, CRM tools). Coverage is broad but not exhaustive, and some niche areas (e.g., global stock exchanges) are over-represented.