BrunoSan AI News Intelligence

news_get_sources

Read-onlyIdempotent

Statistics for canonical source objects, historical article domains and feed operations.

Shows which domains contribute the most articles, total database stats,
and daily averages. Use this to understand the data foundation behind
the news answers, or to verify source diversity and coverage.
Best for: 'what sources does this data come from?', transparency checks.

Example queries:
    - "Which German AI news sources do you cover?"
    - "What are the top 10 most active sources by article volume?"
    - "Which sources cover AI news in Japanese or Chinese — volume and daily averages for Asian market coverage assessment"

Args:
    params (SourcesInput):
        - limit (int): Number of top sources to return, default 20

Returns:
    str: JSON with fields:
        - db_stats (dict): vollstaendiges Schema, BSN-PATCH docstring 20260731_2145
            total_articles                — Artikel gesamt
            canonical_source_objects      — Eintraege in source_registry
            active_canonical_sources      — davon is_active=1
            historical_article_domains    — distinct Domains in news_articles
            unique_sources                — ALIAS von historical_article_domains,
                                            identischer Wert, aus Kompatibilitaet
                                            erhalten
            operative_feeds               — Summe active_feed_count
            validated_feed_records        — Summe validated_feed_count
            total_entity_mentions         — Zeilen in article_entities
                                            (hiess frueher total_entities)
            unique_entities               — Objekte in entities
            daily_avg_articles            — Mittel je Crawl-Tag
        - top_sources (list): Sources with domain and article_count

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

A4/5.0

Behavior4/5

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

Annotations already declare readOnlyHint=true and idempotentHint=true, and the description aligns with a read-only statistics operation. It adds a full description of the return payload including db_stats fields and top_sources, and explains aliases (e.g., unique_sources as alias for historical_article_domains). No contradiction 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.

Conciseness3/5

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

The description is well-structured with summary, usage, examples, args, and returns. However, it contains internal noise such as 'vollstaendiges Schema, BSN-PATCH docstring 20260731_2145' and German field translations that do not serve the agent. It is longer than necessary without each sentence earning its place.

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?

The output schema is complex and the description covers it thoroughly, listing every db_stats field and their meanings. Example queries and limits are provided. Missing api_key mention is a small gap, but the overall description is sufficiently complete for correct invocation.

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 description documents limit with its default value, matching the schema, but omits the api_key parameter entirely. Given the context signal of 0% schema description coverage, the description only partially compensates by covering one of the two parameters. The missing api_key means an agent relying on the description alone wouldn't know about authentication.

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 states a clear verb and resource: 'Statistics for canonical source objects, historical article domains and feed operations' and 'Shows which domains contribute the most articles'. It is easily distinguished from siblings like news_search or news_get_entity by focusing on aggregate source stats, and the 'Best for' examples clarify the exact use case.

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?

Explicit usage context is provided: 'Use this to understand the data foundation behind the news answers, or to verify source diversity and coverage' plus example queries. It does not explicitly name alternatives or state when not to use it, but the context is clear enough for an agent to route to this tool for source-level statistics.

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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