get_market_news

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

With no annotations, the description carries the full burden. It discloses ordering ('newest first') and the dataset's curated, non-live nature, which are valuable behavioral traits. It does not cover pagination or return field details, but as a read-oriented search tool, the disclosed traits are sufficiently transparent.

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 the first front-loading the verb and resource, and the second adding a caveat. There is no filler, and every phrase earns its place.

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 9 parameters, no output schema, and no annotations. The description provides context about the dataset type, filter options, ordering, and curation status. The schema already documents parameters, so the description fills the main gaps, though a note on returned event fields would make it more complete.

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 baseline is 3. The description lists filters already present in the schema and adds no additional syntax or format details beyond them. The 'newest first' note pertains to output ordering, not parameter semantics.

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 states a specific verb ('Search') and a clear resource ('market news & events dataset'), and enumerates filter dimensions. This differentiates it from sibling tools like get_market_sentiment, which focuses on sentiment rather than news/event search.

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 clearly indicates when to use the tool: when searching a market news and events dataset with various filters. However, it does not explicitly mention when not to use it or name alternative tools, so it lacks explicit exclusions.

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