Newsline

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

With no annotations, the description carries the burden. It explains the concept of blindspots and the bias score, but does not disclose response format, pagination, or whether limit defaults are applied. The read-only nature is implied but not stated. It adds moderate value beyond the tool name.

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, each earning its place: the first defines the tool's output, and the second explains the bias scoring and use case. It is concise, front-loaded, and free of fluff.

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 only one parameter, no output schema, and no annotations, the description offers sufficient context for an agent to understand what the tool does and when to use it. It explains the underlying concept and scoring, but lacks explicit details about the return structure or limit behavior.

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 coverage is 100% for the only parameter (limit), which is described as 'Max stories, up to 200.' The description does not mention limit, so it adds no extra meaning beyond the schema. Baseline 3 applies.

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 the tool returns stories covered by only one side of the political spectrum, distinguishing it from the sibling tool get_news. It also explains the bias score scale, adding specificity to the resource.

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 a clear use case: 'finding what a given audience is not being told.' It implies this tool is for blindspots rather than general news, but does not explicitly name when not to use it or compare with get_news.

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

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

No annotations are provided, so the description carries the full burden. It discloses the bias scoring system and the clustering behavior for view=stories, adding useful context. However, it does not mention potential limitations such as result formatting, pagination, or rate limits, which are relevant for a news aggregation tool.

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 long, front-loads the core purpose, and provides essential details about the bias score and view modes without any fluff. Every sentence 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?

For a simple read-only headlines tool with a fully described schema, the description conveys the key data characteristics (22 outlets, bias scoring, view modes) adequately. It does not have an output schema, but the description gives enough context about what results will look like (flat feed or clustered). Minor gaps like exact output fields are not critical for this level of complexity.

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 covers all parameters with descriptions, so the baseline is 3. The description enriches the meaning of the 'view' parameter by explaining 'reverse-chronological feed' and 'group the same event', going beyond the schema's enum labels. This adds genuine value for parameter understanding.

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 ('get') and resource ('current headlines from 22 news outlets across the political spectrum'), clearly defining the tool's function. It also introduces the bias scoring system, which distinguishes it from the sibling 'get_blindspots' that likely focuses on gaps in coverage.

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 explains when to use view=latest versus view=stories, but it does not address when to use this tool versus the sibling get_blindspots. There are no explicit exclusions or alternative tool mentions, leaving the decision to the agent.

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