[HR & Compensation] Search H1B visa salary data. Returns employer, job title, salary, location, and case status.
Args: job_title: Job title to search (e.g. 'software engineer', 'data scientist') company: Company name (e.g. 'Google', 'Microsoft') location: City or state (e.g. 'San Francisco', 'CA')
| Name | Required | Description | Default |
|---|---|---|---|
| company | No | ||
| location | No | ||
| job_title | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate readOnlyHint=true and openWorldHint=true. The description adds that the tool returns specific data fields and that all parameters are optional (defaults to empty string), which is useful context 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.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise: a brief two-sentence overview followed by a structured parameter list. Every sentence adds value, with no extraneous text.
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 the purpose, parameters, and return fields, which is sufficient for a search tool. However, it does not mention pagination, result limits, or behavior when all parameters are empty, leaving minor gaps.
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%, but the description thoroughly explains each parameter with concrete examples (e.g., 'job_title: Job title to search (e.g. 'software engineer', 'data scientist')'). This fully compensates for the lack of schema documentation.
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 and lists the returned fields (employer, job title, salary, location, case status). It distinguishes from sibling tools like hr-compensation__search_salaries by specifying 'H1B visa' context.
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 H1B salary queries but provides no explicit guidance on when to use this tool vs alternatives such as hr-compensation__search_salaries or job-market__salary_search. No 'when-not' or alternative references are given.
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.