BrunoSan AI News Intelligence

news_trending

Read-onlyIdempotent

Top topic clusters for a given day — what is moving the AI world right now?

Returns grouped topic bundles ranked by burst_score (with article_count as
tiebreaker), each with a representative title, top entities, and counts.
Clusters are formed by the BrunoSan news pipeline from daily article intake.
Best for: daily briefings, 'what happened in AI today?', trend detection.

Example queries:
    - "What are today's trending AI topics?"
    - "What were the top AI clusters on March 15th, 2026?"
    - "Today's top 5 trending clusters — dominant entities and event types, not just headlines"

Args:
    params (TrendingInput):
        - date (str): Date YYYY-MM-DD, empty = latest available day
        - limit (int): Number of clusters to return, default 10

Returns:
    str: JSON with fields:
        - date (str): Date used for query
        - total_clusters (int): Available clusters for this day
        - clusters (list): Each with cluster_id, label, topic, event_type,
          article_count, burst_score, representative_title, source, url, top_entities

Input Schema

TableJSON Schema

Name	Required	Description	Default
`params`	Yes

Output Schema

TableJSON Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedSep 14, 2026

TDQS

A4.1/5.0

Behavior4/5

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

Annotations already establish read-only, idempotent, non-destructive behavior. The description adds useful context by explaining that clusters are formed by the BrunoSan pipeline from daily article intake, and ranked by burst_score with article_count as tiebreaker. No contradictions with annotations.

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 front-loaded with the core concept and then organized into best-for, examples, args, and returns. It is a bit long, but each section earns its place and supports agent decision-making.

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 read-only trending tool, the description covers data source, ranking logic, parameter behavior, and return fields. It could be more explicit about the api_key requirement and how it complements sibling tools, but nothing essential is missing.

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

Parameters3/5

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

The Args section explains date format, empty-date behavior, and default limit, but much of this is also present in the input schema. The api_key parameter is not described in prose, and the description mostly restates schema-level details rather than adding deeper semantics.

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 opening sentence precisely identifies the resource ('top topic clusters for a given day') and the conceptual frame ('what is moving the AI world right now?'). The ranking criteria and returned fields make it clearly distinct from siblings like news_search or news_daily_digest.

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

Usage Guidelines4/5

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

The 'Best for' section and example queries give explicit, actionable use cases: daily briefings, trend detection, and 'what happened in AI today?'. It does not explicitly state when not to use alternatives, but the guidance is strong enough to scope selection.

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

All BrunoSan AI News Intelligence tools Inspect

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