get_articles_for_digest

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

With no annotations provided, the description takes on the full burden of behavioral disclosure. It explains the output is raw article data in markdown list format, describes the `topic` pre-filter as whole-word match, and notes that omitting it returns everything. It does not cover error behavior or limits, but for a simple read tool it provides substantial behavioral context.

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 appropriately sized: the first sentence states the core purpose, and the second paragraph adds essential parameter guidance. Every sentence earns its place, with no filler or repetition of schema defaults.

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 has an output schema and the description already explains parameters and usage, the description is mostly complete. It could mention ordering or limits, but the existing text covers the key decisions for selecting and invoking the tool, especially in the context of sibling tools like `make_digest` and `search_archive`.

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 parameter descriptions (0% coverage), so the description must compensate. It fully explains both parameters: `days` defines the time window, and `topic` is an optional whole-word keyword pre-filter, with advice on when to include or omit it. This adds significant meaning beyond the raw schema.

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 uses a specific verb ('Return raw article data') and clearly states the resource (articles from the last `days` days) and output format (markdown list). It also distinguishes itself from sibling tools like `make_digest` by specifying it only provides source material for the host model to write the digest itself.

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 the tool: 'Use when you (the host model) should write the digest yourself.' It also provides usage guidance for the optional `topic` parameter, including when to omit it. It does not explicitly name sibling alternatives, but the context makes the intended use clear.

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