Every archived Korean brokerage report for one KRX ticker, newest first: target price, rating, brokerage, analyst, the Korean title with its English translation, and a one-line English summary. Params: ?limit=20 (max 50). Costs $0.05 in USDC per call, settled via x402.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Max reports (default 20, max 50) | |
| ticker | Yes | 6-digit KRX code, e.g. 005930 |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It discloses ordering, returned fields, the limit cap, and the $0.05 USDC fee settled via x402. It does not mention pagination or error cases, but for a read-only data retrieval tool this is solid coverage.
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 two sentences with no wasted words. The primary purpose and ordering are front-loaded, and the cost and limit details are concise and relevant.
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?
For a simple list tool with two documented parameters and no output schema, the description covers the returned fields, ordering, limit, and cost. Missing details like pagination and explicit alternative tool comparisons are minor gaps.
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 100%, so the schema already documents both parameters. The description adds the default limit and cost context, but does not add significant semantic meaning beyond what the schema provides.
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 verb and resource: it returns every archived Korean brokerage report for one KRX ticker. It also lists the exact fields returned, making it easy to distinguish from other research tools.
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 description conveys clear usage context: use this tool when you need per-ticker brokerage reports, newest first. It does not explicitly name alternatives or state when not to use it, but the one-ticker scope and sibling tool names make the intended use reasonably evident.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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Each tool targets a fairly distinct resource and action: market snapshots vs. single tickers, current vs. historical vs. statistical kimchi premium, headlines vs. English headlines, and per-company vs. latest vs. consensus research. The main ambiguity is between preview_research and preview_research_coverage, and korea_market_snapshot overlaps with several individual data tools, but descriptions clarify the granularity and combined-briefing intent.
All tool names use a consistent lowercase snake_case convention with clear domain prefixes like crypto_krw_, kimchi_premium_, news_, preview_, and research_. The English variant news_headlines_en and the aggregate korea_market_snapshot still follow the same readable naming style.
Fifteen tools sit at the upper boundary of a well-scoped set, but each serves a clear purpose in a paid API that exposes market data, kimchi analytics, news, research, and free previews. The set is slightly broad because it spans two domains and includes aggregate and preview endpoints, but the count is defensible.
Core workflows are well covered: current and historical market data, USD/KRW, kimchi premium current/history/stats, translated news, per-company research, latest research, and consensus. Gaps are minor and mostly outside the stated scope, such as full-text news articles, historical crypto OHLC, or advanced research filtering by sector or brokerage.