[Job Market] 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 description adds value by listing return fields (employer, salary, location) and is consistent with annotations readOnlyHint=true and openWorldHint=true. It does not disclose unexpected behaviors, but with annotations covering safety, this is adequate.
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 two sentences plus parameter list. No wasted words, though the parameter list could be integrated more cleanly. Front-loaded with purpose.
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?
For a simple tool with 2 parameters and no output schema, the description provides enough to use the tool. However, it lacks details on data source reliability, pagination, or rate limits, which would improve completeness.
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?
Despite 0% schema description coverage, the tool description provides helpful parameter explanations: job_title is the job title with an example, and location is optional with an example. This compensates well for missing schema descriptions.
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 H1B visa salary data by job title and returns employer, salary, location. It distinguishes from similar tools like hr-compensation__search_h1b_salaries through the job-market prefix and focus on salary data, but could be more explicit about the data source.
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?
No guidance on when to use this tool vs. alternatives. The description only states what it does without indicating prerequisites, exclusions, or comparisons to sibling tools like hr-compensation__search_salaries.
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.