list_guidesListe les guides de référence de l0g.fr (pages piliers durables : 13F, Form 4, GENIUS Act, OFAC, MiCA...), avec titre, URL, description et résumé définitionnel.
| Name | Required | Description | Default |
|---|---|---|---|
| language | No | Langue optionnelle : fr ou en. Sans filtre, liste bilingue. |
| Name | Required | Description | Default |
|---|---|---|---|
| count | No | ||
| error | No | ||
| guides | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already cover readOnlyHint, idempotentHint, and destructiveHint. The description adds useful context about scope and output fields, but it does not disclose ordering, pagination, or freshness. Given the annotations, a mid score is appropriate.
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 dense sentence includes the action, resource, representative examples, and output fields. There is no filler, and the core information is front-loaded.
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?
This is a simple read-only tool with one optional parameter, full annotation coverage, and an output schema. The description sufficiently covers scope and examples; return values are already handled by the output schema.
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?
Schema coverage is 100%, and the only parameter, language, is fully documented with an enum and description. The tool description adds no substantial parameter semantics beyond the schema, so the baseline of 3 applies.
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 action ('Liste') on a precise resource ('les guides de référence de l0g.fr'), gives concrete topic examples, and lists the returned fields. This clearly separates it from siblings like list_sources or list_recent_analyses by domain, even without naming them.
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?
Usage is implied: an agent can infer this tool is for retrieving a catalog of durable reference guides. However, the description does not explicitly name alternatives or conditions for when to use another sibling tool.
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.