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French family benefits (CNAF corpus)

law_inspect

Read-onlyIdempotent

Inspecteur d'évaluation sur un document d'évaluation ENREGISTRÉ : summary — la fiche d'origine, la vérification de resultHash et de l'instantané, les listes de résultats, de positions et d'issues ; slice — la clôture des nœuds de preuve pour une question, une règle, une position ou un nœud avec leurs adresses, les deux côtés de BOTH, les défaites, les valeurs, les noms résolus d'après l'instantané ; diff — ce qui a changé entre deux documents aux niveaux du contexte, du résultat et de la justification ; locate — la déclaration par StableId. Rien n'est évalué ni recalculé : la source est evaluation de law_ask(proof=true) ou un answerResource. Une donnée absente est un état de limite (limits), jamais une négation.

Input Schema

NameRequiredDescriptionDefault
pageNo
indexNo
bundleNo
selectNo
againstNo
contextNo
sourcesNo
snapshotNo
operationYes
evaluationNo
answerResourceNo

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.8/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnly/idempotent/non-destructive, but the description adds real behavioral semantics beyond them: nothing is evaluated or recalculated, and absent data is a limit state, never a negation. That last point is a meaningful caveat an agent would otherwise get wrong when interpreting empty results.

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?

One dense paragraph, front-loaded with the operation breakdown and closing with the limit-semantics caveat. Every clause carries information, though the long compound sentences make it harder to scan than a short bulleted breakdown would.

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

Completeness3/5

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

With an output schema and annotations present, return values and safety need not be restated, and the description covers operations and limit semantics. However, for an 11-parameter nested schema at 0% coverage, several structural parameters remain undocumented anywhere, leaving the definition incomplete for correct invocation.

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 0% across 11 parameters, so the description must carry the load. It clarifies the operation modes, the select targets (question, rule, position, node), the two-document nature of diff, StableId lookup for locate, and the evaluation/answerResource source — but leaves page, budgetBytes, index, bundle, context, sources and snapshot entirely unexplained.

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 names a specific read-only inspection tool over a recorded evaluation document and then defines each of the four operation modes (summary, slice, diff, locate) with the resource each one acts on. It even signals provenance (law_ask(proof=true) or answerResource), which helps separate it from sibling tools like law_explain or law_query.

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

Usage Guidelines3/5

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

The text states the accepted input source and that nothing is recomputed, which implies when the tool applies, but it never explicitly contrasts usage against siblings such as law_explain, law_query or law_rules, nor states when not to use it. Usage is inferable rather than prescribed.

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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