news-sentiment

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries full burden. It discloses data sources (GDELT, Google News), scoring scale (-100 to +100), lookback range (1-30 days, default 3), and caching (10 min). This provides good transparency for an API tool. No contradictions.

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 three sentences, front-loaded with the core purpose. Every sentence adds value: source details, return fields, and parameters. No waste. Efficiently structured for quick parsing.

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, the description explains the return type (article count, sentiment score, headlines, domains) adequately. It also covers caching and source fallback. It could mention the number of headlines returned or pagination, but it is sufficiently complete for typical use.

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?

Both parameters (query, days) are described in the schema with full coverage (100%). The description reiterates the lookback window and default, adding no new parameter-specific meaning. The baseline 3 applies since the schema already handles parameter documentation.

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 that it returns global news coverage and sentiment for companies, stock tickers, or topics, specifying the primary and fallback sources and the returned data (article count, sentiment, headlines, domains). However, it does not explicitly differentiate itself from sibling tools like global-news-intel, which also cover news.

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 implies use for news sentiment analysis but does not provide explicit when-to-use or when-not-to-use guidance, nor does it mention alternatives among sibling tools. The context is clear but lacks direct comparison.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.