[Job Market] Get Glassdoor reviews for a company. Returns ratings, pros, cons, salary data. Args: company: Company name (e.g. 'Google') max_results: Max reviews (default 20)
| Name | Required | Description | Default |
|---|---|---|---|
| company | Yes | ||
| max_results | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true. Description adds that it returns ratings, pros, cons, salary data, but does not disclose rate limits, authentication, or other behavioral traits. Adequate given 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?
Two concise sentences plus parameter details. Front-loaded with purpose. No unnecessary words.
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?
Covers purpose, parameters, and return content adequately for a simple tool. No output schema, but description lists high-level data types. Minor gap: return format not specified.
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 has no descriptions (0% coverage). Description explains company with example 'Google' and max_results with default, adding significant meaning beyond the 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?
Clearly states it gets Glassdoor reviews for a company, lists what it returns (ratings, pros, cons, salary data). Distinct from siblings by source and content.
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 explicit when-to-use or alternatives mentioned. Agent can infer if Glassdoor reviews are needed, but no guidance on when not to use or compare with similar tools like 'job-market__salary_search'.
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.