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search_news

Search news articles with bias analysis, filtering by source, category, and social shares to find relevant information.

Instructions

Search news articles.

Returns a list of matching articles. Each article includes:
- title, source, date, link, category, rank, shares, summary
- bias_values: dict of per-dimension bias scores using plain-text keys (e.g. 'liberal conservative bias'),
  same schema as get_bias_from_url and get_all_source_biases (when available)
- context: AI-generated contextual background for the article (when available)
- raw_data: additional raw metadata fields (when available)

Args:
    query: Search keywords (required).
    limit: Max results (1-100, default 20).
    source: Filter by source name, e.g. 'CNN', 'Reuters'.
    category: Filter by category. One of: 'trending', 'tech', 'markets', 'politics',
              'business', 'science', 'memes'.
    days_back: Only include articles from the last N days. 0 means no date filter. Default: 720 (2 years).
    min_shares: Minimum total social shares.
    sort: Sort order. One of: 'rank' (relevance, default), 'date' (newest), 'shares' (most shared).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYes
limitNo
sourceNo
categoryNo
days_backNo
min_sharesNo
sortNorank

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior3/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It describes the return format in detail (list of articles with specific fields) and mentions availability conditions ('when available'), which adds useful context. However, it lacks information on rate limits, authentication needs, or error handling, leaving gaps for a mutation-free search tool.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured and appropriately sized. It starts with the core purpose, details the return format, and lists parameters with clear explanations. While slightly verbose due to the parameter details, every sentence adds value, and it's front-loaded with key information.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (7 parameters, no annotations) and the presence of an output schema (implied by 'Returns a list'), the description is largely complete. It thoroughly documents parameters and return values, though it could benefit from more behavioral context (e.g., rate limits). The output schema reduces the need to explain returns in depth.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The description adds significant meaning beyond the input schema, which has 0% schema description coverage. It explains each parameter's purpose, constraints (e.g., '1-100' for limit, enum values for category and sort), and defaults, fully compensating for the schema's lack of documentation. This is essential given the 7 parameters with minimal structured info.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'Search news articles.' It specifies the verb ('search') and resource ('news articles'), making the function unambiguous. However, it doesn't explicitly differentiate from sibling tools like 'search_balanced_news' or 'search_memes', which prevents a perfect score.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention sibling tools like 'search_balanced_news' or 'search_memes', nor does it specify prerequisites or exclusions. Usage is implied by the parameters but not explicitly stated.

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

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