Use Cases

The tools make more sense when you see them working together. These are complete workflows — the prompt, the tools called, the actual data returned, and what the agent does with it.

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Security risk monitoring

"Are there any new AI security threats I should know about? Especially around agents and reasoning models."

Tools called: search_news → get_news_detail

The agent searches for recent security-related events, then pulls full detail on the most relevant result — including semantically related stories that provide context without requiring more queries.

All three results are directly relevant — agent security risks, autonomous jailbreaks, real-world failure modes. The agent picks the highest-similarity result and fetches the full detail with related stories.

What the agent gets:

• similarity scores show which results are semantically closest to the query, not just keyword matches
• news_type distinguishes verified reporting from unverified rumors — critical for security assessments
• source_account + main_url give the agent a primary source to cite or read further

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Morning briefing

"Give me today's most important AI news."

Tools called: get_latest_news(impact: "HIGH", limit: 5)

No search query needed. The agent pulls the curated HIGH-impact feed directly — stories that Hyperterminal's pipeline scored as significant across hardware, research, and safety.

These 5 results were pulled from ~90+ events indexed in the past 48 hours. The agent didn't scan headlines — it got the filtered signal.

What the agent gets:

• impact_level: "HIGH" filter replaces manual curation — the pipeline already scored everything
• entity_slugs on each event let the agent spot cross-company stories at a glance (AMD + Microsoft + OpenAI in one event)
• content_type distinguishes breaking news from research papers and product releases in the same feed

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Competitive landscape tracking

"What's trending in AI this week? Who's making moves?"

Tools called: get_trending(days: 7) → get_latest_news(entity: ["nvidia"])

The agent gets a quantified view of who and what is getting covered, then drills into the fastest-moving entity.

NVIDIA is up 240% week-over-week. The agent follows up with get_latest_news(entity: ["nvidia"]) to get the actual stories behind that number.

What the agent gets:

• changePercent is the signal — it's not just who has the most coverage, it's who's accelerating
• Three dimensions in one call: entities, categories, and models — different angles on the same week
• trend: "down" on Google (-58%) and safety (-53%) is as informative as what's trending up

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Weekly strategic brief

"Summarize what happened in AI last week."

Tools called: get_weekly_digest()

The heaviest single-call response in the API. 94 events processed down to a structured brief with editorial analysis, "why it matters" context for each key development, AGI probability tracking, and a podcast link.

94 raw events. 21 flagged high-impact. 3 key developments surfaced with source URLs and explicit reasoning about why each one matters. The agent doesn't need to synthesize — it's done.

What the agent gets:

• whyItMatters on each key development — not just what happened, but the implication. This is the part that takes time to write manually
• agi_probability + agi_probability_delta — weekly AGI tracker with directional movement and category breakdown
• week_in_numbers — three stats ready to drop into a slide or report
• podcast_url — 6-minute AI-generated audio digest if the recipient prefers listening

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Deep research on a topic

"Tell me everything about NVIDIA's recent AI investments."

Tools called: get_entities(type: "company") → search_news("NVIDIA AI investment") → get_news_detail(id, include_related: true)

The agent starts by confirming the correct entity slug, then searches, then expands the most relevant result with semantically related stories to build a full picture.

Three related stories surface automatically — a biotech deal, an inference processor rumor, and the Meta partnership — all semantically connected to the original story without additional queries.

What the agent gets:

• similarity scores on related stories make it easy to rank follow-ups by relevance — 0.77 is strong signal
• Related stories span a 2-week window, building a timeline of NVIDIA's investment activity automatically
• get_entities first ensures the slug is right before filtering — avoids zero results from a typo or alternate name

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