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l0g.fr Risk Intelligence

Get Article

get_article
Read-onlyIdempotent

Renvoie le texte d'une analyse ou d'un guide l0g à partir de son slug. Le résultat est paginable par offset/length et expose nextOffset pour récupérer la suite. Utiliser section=tail ou section=sources pour atteindre rapidement conclusion, limites, méthodologie et sources. Pour lire le document complet comme objet, utiliser aussi la ressource l0g://articles/{slug} ou l0g://guides/{slug}.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
slugYesSlug de l'article ou du guide.
limitNoAlias de length, recommandé pour les clients agents.
cursorNoCurseur opaque nextCursor renvoyé par un appel précédent.
lengthNoLongueur maximale du segment renvoyé.
offsetNoPosition de départ en caractères pour paginer le texte.
sectionNoSection pratique : body avec offset, head, tail ou sources.body
languageNoLangue optionnelle : fr ou en. Une URL /en/... permet aussi de l’inférer.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlNo
slugNo
textNo
typeNo
errorNo
limitNo
titleNo
wordsNo
lengthNo
offsetNo
hasMoreNo
sectionNo
languageNo
textCharsNo
truncatedNo
nextCursorNo
nextOffsetNo
referencesNo
totalCharsNo
totalWordsNo
canonicalIdNo
sectionFoundNo
translationStatusNo

TDQS

A4.4/5.0
Behavior4/5

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

Annotations already mark it read-only and idempotent; the description adds pagination behavior (offset/length, nextOffset) and section shortcuts beyond that. 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.

Conciseness5/5

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

Four sentences, tightly focused: purpose, pagination, section shortcuts, and alternative resource. The primary action is front-loaded and each sentence adds distinct value, with no filler.

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 7-parameter tool with a full output schema, the description covers purpose, pagination, section selection, and the alternative full-object resource. The only small gap is that it doesn't explain how cursor/nextCursor relates to nextOffset, but that is documented in the schema.

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 coverage is 100%, but the description enriches the section parameter by mapping tail to conclusion/limitations/methodology and sources to sources, and clarifies that offset/length drive the returned segment. It doesn't repeat schema details, so it adds meaning beyond the parameter descriptions.

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 names a specific verb ('Renvoie'), a concrete resource (analysis or guide l0g), and the retrieval key (slug). The text-returning behavior distinguishes it from the sibling list/search tools, so an agent can tell it apart without opening the schema.

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?

It gives clear usage context: use section=tail or section=sources for fast access to conclusion, limitations, methodology, and sources, and mentions the l0g://articles/{slug} alternative for full-object reads. It does not explicitly contrast with sibling tools like search_content or list_recent_analyses, so it misses the 'when-not-to-use' part.

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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TDQS

A3.9/5.0
Disambiguation4/5

Most tools target clearly distinct resources (articles, claims, sources, indices, signal history, integrity), and the descriptions are detailed. The two real ambiguities are get_changefeed vs get_changes, which are near-identical except for filtering, and the Agent Surface metadata cluster (manifest, openapi, integrity, verify, ndjson feed) where an agent could misselect. These are minor enough not to undermine the set.

Naming Consistency5/5

All 21 tools follow a consistent verb-first snake_case pattern with clear semantic verbs: get_ for retrieval, list_ for enumeration, search_ for querying, plus build_research_pack and verify_artifact as lone but clearly communicative composites. There is no mixing of conventions or vague verbs like process or run, making tool selection predictable.

Tool Count4/5

21 tools is above the ideal 3-15 range, but the server covers a broad domain: content retrieval, claims and evidence graphs, risk signals, change monitoring, and integrity verification. A few tools could be consolidated (the changefeed pair and the metadata cluster), but none is pure filler, so the count feels justified though slightly heavy.

Completeness4/5

For a read-only intelligence platform, the coverage is comprehensive: content listing and full-text retrieval, claims and evidence graphs, source auditing, risk indices with history, freshness and risk-diff monitoring, and artifact verification. Minor gaps include no per-source detail endpoint and no push or subscription mechanism, but agents can work around these using list_sources and the changefeed.