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l0g.fr Risk Intelligence

Search By Topic

search_by_topic
Read-onlyIdempotent

Liste les analyses rattachées à un sujet (hub thématique) de l0g.fr. Accepte un slug ou un libellé approchant ; les sujets disponibles sont renvoyés avec une erreur de résolution.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoNombre maximum d'analyses.
topicYesSlug ou libellé du sujet.
languageNoLangue optionnelle : fr ou en. Sans filtre, recherche bilingue.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
countNo
errorNo
labelNo
topicNo
topicsNo
analysesNo
requestedNo

TDQS

A3.9/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is covered. The description adds useful behavior beyond annotations: it accepts a slug or approximate label and, on resolution failure, returns the available topics. This helps the agent anticipate tolerant input and recover from invalid topic values.

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?

The description is a single compact sentence with two clauses: the main action is front-loaded, and the error behavior is appended without repetition. Every clause earns its place, and there is no filler or redundant restating of the tool name.

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 search tool with three simple parameters and an output schema, the description plus schema cover the inputs, tolerance behavior, and failure mode. The main omission is explicit routing guidance between this tool and search_content, though that gap is already captured under usage_guidelines.

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?

The schema covers all three parameters with descriptions, so the baseline is 3. The description adds meaningful nuance for the topic parameter by specifying 'slug ou un libellé approchant' and clarifying that unresolved topics trigger a response containing available topics. This goes beyond the schema's one-line parameter descriptions.

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

Purpose4/5

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

The description states a specific verb and resource: 'Liste les analyses rattachées à un sujet (hub thématique) de l0g.fr.' This clearly identifies a topic-scoped search tool, but it does not explicitly differentiate it from sibling tools such as search_content, so it stops short of full sibling differentiation.

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?

The description implies its usage context by mentioning that it accepts a slug or approximate label and returns available topics on resolution failure. However, it gives no explicit guidance on when to prefer this tool over alternatives like search_content or list_recent_analyses, leaving selection largely to inference.

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.