l0g.fr Risk Intelligence

List Sources

list_sources

Read-onlyIdempotent

Liste les sources primaires institutionnelles suivies par l0g et les hôtes effectivement cités par les claims. Utile pour auditer l'origine des preuves.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`mode`	No	Type de sources à renvoyer.	both
`limit`	No	Nombre maximum de sources ou hôtes.
`sourceId`	No	Source précise à résoudre, par slug, nom ou hôte.
`includeClaims`	No	Inclut les claims associées quand sourceId est fourni.

Output Schema

TableJSON Schema

Name	Required	Description	Default
`error`	No
`claims`	No
`counts`	No
`source`	No
`version`	No
`sourceId`	No
`sourceType`	No
`claimsCount`	No
`sourcePolicy`	No
`primarySources`	No
`referenceHosts`	No

TDQS

A4.1/5.0

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 context about the scope of the data returned, but it does not disclose additional behavioral traits such as pagination behavior, result limits, or any response characteristics beyond what the output schema would already provide.

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 one focused sentence with a short clarifying use-case clause. It is front-loaded with the verb and resource, contains no fluff, and every part contributes to understanding the tool's purpose.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the rich annotations, full parameter descriptions in the schema, and presence of an output schema, the description is sufficient for an agent to call this tool correctly. It communicates the tool's scope and intended use without leaving critical gaps.

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?

Schema description coverage is 100%, and every parameter already has a meaningful description in the schema. The tool description does not add extra meaning about parameters, so the baseline score of 3 applies.

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

Purpose5/5

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

The description clearly states the tool lists primary institutional sources tracked by l0g and hosts actually cited by claims, which is a specific verb+resource combination. It distinguishes list_sources from sibling list tools like list_guides and list_recent_analyses by naming the exact entities returned.

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

Usage Guidelines4/5

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

The description provides a clear use case: auditing the origin of evidence. It does not explicitly mention when not to use this tool or name alternatives, but the stated purpose is enough for an agent to select it in the appropriate context.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All l0g.fr Risk Intelligence tools Inspect

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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.

List Sources

Input Schema

Output Schema

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TDQS

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