[Global Equities Screener] Screen Hong Kong-listed stocks via HKEX (Hang Seng + Mainboard + GEM).
Args: criteria: filter dict (industry, market cap, P/E, dividend yield, etc.) limit: max rows to return
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| criteria | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, so no contradiction. The description adds useful context about the filter dictionary (e.g., industry, market cap, P/E) and the limit parameter, giving the agent a clearer understanding of capabilities 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?
Description is extremely concise: one sentence for purpose plus two lines for arguments. All information is front-loaded and every word earns its place. No unnecessary repetition.
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?
Tool has no output schema and the description does not specify return format (e.g., fields returned, pagination). For a screening tool with filters, this omission is significant. The description is incomplete for an agent to fully understand the tool's output.
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 0% description coverage, but the description explains both parameters: 'criteria' as a filter dict with examples, and 'limit' as max rows. This adds significant meaning beyond the bare schema, though the exact format of the dict is not fully specified.
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?
Description clearly states it screens Hong Kong-listed stocks via HKEX (Hang Seng + Mainboard + GEM), with specific verb 'screen' and resource 'Hong Kong-listed stocks'. The sibling tools are differentiated by exchange (e.g., screen_australia_asx), so this tool's purpose is unambiguous and distinct.
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?
Description implies use for Hong Kong stocks and lists covered markets, but does not explicitly state when to use this tool versus alternatives (e.g., other screeners). However, the sibling naming convention makes the context clear, so the guidance is adequate but not explicit.
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.