get_risk_indicesIndices de risque publiés par l0g.fr (tableaux de bord macro US et zone euro, Yen Carry, Energie, Dette US) à la cadence des snapshots, plus un résumé de la confluence 13F. Pas de temps réel strict, pas un conseil en investissement.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
| Name | Required | Description | Default |
|---|---|---|---|
| error | No | ||
| source | No | ||
| indices | No | ||
| snapshot | No | ||
| generated | No | ||
| confluence | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so safety is covered. The description adds useful behavioral context beyond annotations: data source, snapshot cadence, non-real-time nature, inclusion of the 13F confluence summary, and a non-advice disclaimer.
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?
The description is compact and front-loaded, packed with the essential specifics: publisher, dashboard coverage, snapshot cadence, 13F summary, real-time caveat, and disclaimer. Every sentence contributes meaning without redundancy.
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 parameterless read-only tool with an output schema and safety-related annotations, the description covers the main operational context: what data is included, from whom, and at what freshness. It could be slightly richer about how the risk indices are structured, but the output schema likely fills that gap.
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 tool has zero parameters, and the schema reflects that with 100% coverage, so there are no parameter semantics for the description to clarify. Baseline for zero parameters is 4; the description appropriately focuses on content rather than parameters.
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 clearly identifies the resource: risk indices published by l0g.fr, including specific dashboards and a 13F confluence summary. It is unambiguous about what the tool returns, though it uses a noun phrase rather than a verb and does not explicitly name a sibling for differentiation.
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 definition implies usage context by noting snapshot cadence and explicitly stating 'Pas de temps réel strict' (not strict real-time), which helps an agent avoid using it for real-time needs. However, it does not name alternative tools or provide explicit when-to-use versus when-not-to-use guidance.
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.