aggregate_news

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

With no annotations, the description carries the burden of disclosing behavior. It explains that it merges similar news, uses a similarity threshold (with default and range), and returns dedup statistics and platform coverage. It does not mention potential edge cases like rate limits or pagination, but for an aggregation tool this is sufficient and adds meaningful value beyond the schema.

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 well-structured with a clear title, body, args list, returns, and examples. It is front-loaded with the core purpose and provides just enough detail without redundancy. Each section earns its place and aids comprehension.

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 tool with 5 parameters and an output schema, the description covers all necessary aspects: parameter semantics, return structure, and usage examples. It does not need to explain the full output schema since one exists, and the summary of returns ('dedup statistics, aggregated news list, platform coverage') is sufficient for an agent to understand what to expect.

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 schema provides no descriptions, but the description compensates fully. It explains every parameter (date_range, platforms, similarity_threshold, limit, include_url) with defaults, accepted values, and meaning (e.g., 'similarity_threshold: 0.3-1.0, default 0.7, higher is stricter'). Examples also clarify usage.

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 a specific verb 'merge' with a resource: 'different platforms reporting the same event' and specifies the output (cross-platform coverage and heat). This distinguishes it from sibling tools like get_latest_news or search_news, which focus on retrieving or searching individual news items rather than aggregating and deduplicating across platforms.

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 provides clear context: use this when you want a deduplicated, cross-platform view of an event. It does not explicitly name alternatives or say when not to use it, but the purpose is self-evident given the aggregation focus and the sibling tools list. The examples further illustrate typical usage.

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