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l0g.fr Risk Intelligence

Get Signal History

get_signal_history
Read-onlyIdempotent

Renvoie les observations datées des séries l0g, leur identité citable, leur version méthodologique, les changements de niveau, l'état courant des scores et la confluence 13FLOW.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
keyNoSignal optionnel : us, eu, yen, energie ou debt.
limitNoNombre maximum d'observations et d'événements retournés.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
errorNo
caveatNo
eventsNo
policyNo
currentNo
filtersNo
updatedNo
versionNo
coverageNo
generatedNo
confluenceNo
instrumentsNo
levelChangesNo
observationsNo

TDQS

B3.2/5.0
Behavior3/5

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 behavioral detail about what the response contains (dated observations, level changes, current scores, etc.), but says nothing about pagination, default limits, or filtering semantics beyond what the schema states.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

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

The description is a single information-dense sentence that front-loads the main returned entity. Its length is justified by the need to enumerate several output categories, though it relies on domain jargon such as 'l0g' and '13FLOW'.

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?

The tool is a simple read-only query with two optional, fully documented parameters and an output schema present, so the description need not restate schema details. The listed output facets plus annotations give sufficient context for an agent to invoke it correctly, though a short usage-preference note relative to sibling tools would make it more complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Input schema coverage is 100%: the 'key' parameter has an explicit enum and description, and 'limit' has default/min/max plus a description. The tool description does not need to re-explain these, so the baseline 3 applies.

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 uses a specific verb ("Renvoie") and resource ("les observations datées des séries l0g") and enumerates the returned data categories (citable identity, methodological version, level changes, scores, 13FLOW confluence). It is clear about what the tool does, though it does not explicitly differentiate it from sibling tools such as get_changes or get_risk_diff.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

There is no guidance about when to choose this tool over the many sibling get_* tools, nor any when-not-to-use or exclusion criteria. The intended use is only implied by the phrase 'Renvoie les observations...' and the tool name.

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.