[Global Equities Screener] Screen Tokyo Stock Exchange (TSE) — Nikkei 225, TOPIX, Prime / Standard / Growth markets.
Args: criteria: filter dict (sector, market cap, P/B, 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 and openWorldHint=true, so the description need not repeat safety. The description adds that it is a screener with filter criteria, which is consistent. No additional behavioral traits (e.g., rate limits, data freshness) are disclosed, making it adequate but minimal.
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 brief with two succinct sentences for the purpose, followed by a clear args list. It is front-loaded with the main action. Minor improvement could be more structured formatting, but overall it is 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?
The description covers the core functionality and parameters. Since no output schema exists, the return format is not explained, but for a screener tool, the output is reasonably implied (list of stocks). The description is complete enough for an agent to use the tool correctly.
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% with criteria having no specific properties described. The description adds essential meaning by listing example filter fields (sector, market cap, P/B, dividend yield) and explaining limit as 'max rows'. This compensates for the schema's lack of detail.
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 the Tokyo Stock Exchange, referencing specific indices (Nikkei 225, TOPIX) and market segments (Prime/Standard/Growth). This differentiates it from sibling tools focused on other exchanges.
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 tool's context is clear: it is for screening Japanese equities on TSE. However, it does not explicitly state when to avoid using this tool or suggest alternatives, leaving the decision to the agent based on sibling tool names.
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.