[Global Equities Screener] Screen ChiNext (Shenzhen growth board) — China's Nasdaq-equivalent for growth companies.
Args: criteria: filter dict (sector, market cap, P/E, 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 and openWorldHint=true. The description adds context that it screens a specific board (ChiNext) and describes args, but does not disclose behavioral traits like pagination, rate limits, or what happens on no results. It does not contradict 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 very short and front-loaded: first line states purpose, then two bullet points for args. No unnecessary words. However, it could benefit from slight restructuring for readability, but overall efficient.
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?
Given no output schema and complex criteria, the description is incomplete. It does not describe what the tool returns (e.g., list of stocks, columns), default behavior, or how to use the criteria dict effectively. The openWorldHint suggests varying results, but no further explanation.
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%, so the description must compensate. It provides brief explanations: 'criteria: filter dict (sector, market cap, P/E, etc.)' and 'limit: max rows to return'. This adds some meaning beyond the bare schema, but the criteria structure is vague and the limit description is minimal.
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 screens ChiNext, a specific Chinese growth market, and correctly identifies it as China's Nasdaq-equivalent. The verb 'screen' and resource 'ChiNext' are explicit, distinguishing it from sibling screeners for other markets.
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 versus alternatives. The description does not mention when to choose ChiNext over other equity screeners, nor does it provide any exclusions or prerequisites. The sibling tools are all different markets, but the description fails to contrast or specify scenarios.
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.