list_recent_analysesListe les analyses (articles) les plus récentes de l0g.fr, de la plus récente à la plus ancienne, avec titre, URL, date, description et thèmes.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Nombre d'analyses à renvoyer. | |
| language | No | Langue optionnelle : fr ou en. Sans filtre, liste bilingue. |
| Name | Required | Description | Default |
|---|---|---|---|
| count | No | ||
| error | No | ||
| analyses | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already mark the tool read-only and non-destructive. The description adds behavioral context by disclosing the sort order, the source site, and the response fields. It does not contradict annotations and provides useful detail beyond the safety hints, while the output schema covers the remaining return structure.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
A single front-loaded sentence contains the action, scope, ordering, and returned fields with no filler or repetition. Every element earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple read-only list operation with two optional parameters and an output schema, the description plus annotations and schema provide everything needed: source, ordering, returned fields, parameter defaults, and enum values.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage for both limit and language, including defaults, constraints, and enum values. The description does not add extra parameter semantics, but the schema already carries the burden fully.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
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 les plus récentes de l0g.fr'), specifies the ordering ('de la plus récente à la plus ancienne'), and enumerates the returned fields (titre, URL, date, description, thèmes). This clearly distinguishes it from siblings like get_article and search_content.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description gives clear context: it is the tool for listing recent analyses from l0g.fr in reverse chronological order. It does not explicitly name alternatives or when-not-to-use cases, but the scope is precise enough that an agent can infer when to select it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
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.
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.
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.
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.