[Global Equities Screener] Screen China's STAR Market (Shanghai Sci-Tech Innovation Board) — Chinese hard-tech & biotech IPOs.
Args: criteria: dict of filters (e.g. {"sector": "Semiconductors", "marketCapMin": 1e10}) 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 no new behavioral traits beyond the example filter, which is useful but not detailed. No mention of side effects, rate limits, or pagination.
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 clear sentences and an args list. No wasted words, and the purpose is front-loaded. Efficiency is excellent.
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 tool has no output schema, and the description does not mention what the output looks like (e.g., list of stocks with fields). Given the complexity of a screener, this is a gap. The limit parameter and openWorldHint provide some context, but output structure is missing.
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 coverage is 0%, so the description bears full burden. It explains 'criteria' as a dict of filters with a concrete example, and 'limit' as max rows. This adds meaning beyond the empty schema, though not all possible filter keys are listed.
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 screens China's STAR Market, mentioning the specific board and types (hard-tech & biotech IPOs). This distinguishes it from sibling tools for other markets like A-shares or ChiNext.
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 market-specific name and description ('Screen China's STAR Market') imply when to use this tool versus other global-equities-screener siblings. However, no explicit 'when not to use' or alternatives are given, but the context is clear enough.
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.