quant_fetch_news

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

Annotations already declare readOnlyHint, destructiveHint, and idempotentHint, which the description complements by detailing behavioral traits: 'Data only — no sentiment is computed' and 'deliberately does no interpretation'. It also specifies the return structure (title, summary, published, provider) and empty-list scenario, adding value beyond 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 concise with front-loaded main action, a brief behavioral note, and a structured Args/Returns section. Every sentence is informative and none are redundant, achieving good density of useful content.

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 the tool's simplicity, the combination of description, schema, and annotations covers all essential aspects: purpose, usage guidance, parameters, return format, and integration with sibling tools. The mention of an output schema (even if not displayed) further supports completeness.

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?

The input schema already provides descriptions for ticker ('US equity symbol, e.g. NVDA') and response_format ('markdown for human-readable or json for machine-readable'). The description restates these fields but adds no new semantic detail. Since schema coverage is high, a baseline of 3 is appropriate.

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 opens with 'Fetch recent headlines for a ticker', clearly stating the verb+resource. It further distinguishes itself by explicitly noting it does not compute sentiment, and directs the agent to use quant_score_decision for interpretation, making the purpose unambiguous.

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 explicitly states when to use this tool: for raw data only, and instructs the agent to form its own sentiment and pass to a sibling tool (quant_score_decision). It also mentions the return format and empty-list handling, providing clear context for usage.

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