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get_news_by_topic

Fetch news articles by selecting a topic from categories like business, technology, sports, and health. Set lookback period, result count, and get summaries or full article data.

Instructions

Find articles by topic using Google News. topic is one of WORLD, NATION, BUSINESS, TECHNOLOGY, ENTERTAINMENT, SPORTS, SCIENCE, HEALTH, POLITICS, CELEBRITIES, TV, MUSIC, MOVIES, THEATER, SOCCER, CYCLING, MOTOR SPORTS, TENNIS, COMBAT SPORTS, BASKETBALL, BASEBALL, FOOTBALL, SPORTS BETTING, WATER SPORTS, HOCKEY, GOLF, CRICKET, RUGBY, ECONOMY, PERSONAL FINANCE, FINANCE, DIGITAL CURRENCIES, MOBILE, ENERGY, GAMING, INTERNET SECURITY, GADGETS, VIRTUAL REALITY, ROBOTICS, NUTRITION, PUBLIC HEALTH, MENTAL HEALTH, MEDICINE, SPACE, WILDLIFE, ENVIRONMENT, NEUROSCIENCE, PHYSICS, GEOLOGY, PALEONTOLOGY, SOCIAL SCIENCES, EDUCATION, JOBS, ONLINE EDUCATION, HIGHER EDUCATION, VEHICLES, ARTS-DESIGN, BEAUTY, FOOD, TRAVEL, SHOPPING, HOME, OUTDOORS, FASHION.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
topicYesTopic to search for articles.
periodNoNumber of days to look back for articles.
full_dataNoReturn full data for each article. If False a summary should be created by setting the summarize flag
summarizeNoGenerate a summary of the article, will first try LLM Sampling but if unavailable will use nlp
max_resultsNoMaximum number of results to return.

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 behavioral burden. It clearly signals a read-only news lookup and names the external data source (Google News), which is the core behavioral trait. It does not mention operational details such as result volume, look-back period, summary generation, or external-service limits, but those are substantially covered by the input schema and output schema.

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 leads with the action sentence and then provides a single essential supporting artifact—the list of allowed topics. There is no filler, though the list is long and formatted as a dense all-caps block; still, each item is information an agent needs.

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?

For a simple retrieval tool with 100% schema coverage and an output schema, the description plus schema is sufficient to call it correctly: the required topic is constrained, defaults are present in the schema, and the return shape is covered by the output schema. It could add a pointer to a keyword-based sibling, but that is more of a usage-guidance enhancement.

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

Parameters4/5

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 adds real value by enumerating the valid topic vocabulary, which the schema does not provide as an enum. Other parameters are already well described in the schema, so the description need not repeat them.

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

Purpose5/5

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

The description opens with a specific action and resource: 'Find articles by topic using Google News.' The 'by topic' qualifier distinguishes it from sibling tools like get_news_by_keyword and get_news_by_location, and the included list of permitted topics makes the domain concrete.

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

Usage Guidelines3/5

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

Usage is implied rather than explicit: an agent can infer this tool is for preset Google News topics as opposed to keyword, site, or location searches, especially from the sibling names. However, the description never states when to prefer this tool over get_news_by_keyword, get_news_by_site, etc., and it provides no negative guidance.

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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