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BGB (Germany)

by io.arxo
OverviewDiscussionsAdmin

law_argue

Read-only

How robust a conclusion is: the map of the dispute under the argumentation profile The answer falls into four classes: SKEPTICALLY_ACCEPTED — the conclusion is invulnerable (it survives in the grounded extension); CREDULOUSLY_ACCEPTED — merely defensible (there is a position for it and a position against); REJECTED — it survives nowhere; NO_ARGUMENT — there are no arguments at all. It builds ALL arguments out of the facts and the rules of the instrument (chains that did not fire in ordinary inference included), the rebut/undermine/undercut attacks and the defeats by priority. Predicate names come from law_packages. On large instruments a refusal by cap is possible — narrow facts or raise limits.maxArguments.

Input Schema

NameRequiredDescriptionDefault
argsNothe arguments of the predicate
factsNofacts of the case: [{predicate, args, provenance, negated?}]. provenance is a required object; {} is allowed, its fields are optional, and evidence is not required. The package EvidencePolicy reads confidence, not the host
limitsNothe caps of the profile: maxArguments (64), maxExtensions (8), maxUndecided (16)
contextNoExpliziter Zeitkontext: timezone ist ein IANA-Name; decisionTime und knowledgeTime sind vollständige Instants mit UTC oder Offset und werden unverändert übernommen. Mit context ist legalTime erforderlich; ein Profil kann alle Felder verlangen
packageNonarrow the question to one package of law
legalTimeNothe date of the law (YYYY-MM-DD)
predicateYesthe conclusion whose robustness is being measured

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed4 schema fields changed
    • addedInput schema / properties / context
      Added value: +{
+  "additionalProperties": false,
+  "description": "Expliziter Zeitkontext: timezone ist ein IANA-Name; decisionTime und knowledgeTime sind vollständige Instants mit UTC oder Offset und werden unverändert übernommen. Mit context ist legalTime erforderlich; ein Profil kann alle Felder verlangen",
+  "properties": {
+    "decisionTime": {
+      "type": "string"
+    },
+    "knowledgeTime": {
+      "type": "string"
+    },
+    "timezone": {
+      "type": "string"
+    }
+  },
+  "required": [
+    "timezone",
+    "decisionTime",
+    "knowledgeTime"
+  ],
+  "type": "object"
+}
    • changedInput schema / properties / facts / description
      Previous value: -"facts of the case: [{predicate, args, negated?, provenance?}]. provenance is the same object as on law_ask: origin, document, quote, extractor. confidence is read by the package EvidencePolicy, not by the engine"New value: +"facts of the case: [{predicate, args, provenance, negated?}]. provenance is a required object; {} is allowed, its fields are optional, and evidence is not required. The package EvidencePolicy reads confidence, not the host"
    • addedInput schema / properties / facts / items
      Added value: +{
+  "properties": {
+    "args": {
+      "type": "array"
+    },
+    "predicate": {
+      "type": "string"
+    },
+    "provenance": {
+      "type": "object"
+    }
+  },
+  "required": [
+    "predicate",
+    "args",
+    "provenance"
+  ],
+  "type": "object"
+}
    • changedInput schema / properties / limits / description
      Previous value: -"the caps of the §274 profile: maxArguments (64), maxExtensions (8), maxUndecided (16)"New value: +"the caps of the profile: maxArguments (64), maxExtensions (8), maxUndecided (16)"
  2. First observed

TDQS

A3.8/5.0
Behavior4/5

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

The annotations already establish that this is read-only, non-destructive, and closed-world. The description adds substantial behavioral context: the four robustness classes, construction of all arguments including non-fired chains, attacks and defeats, and a possible cap refusal with remediation.

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 purpose is front-loaded and the four result classes are compactly described. The paragraph is dense and jargon-heavy, but most sentences add distinct information about output classes or operational limits.

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?

This is a complex tool with nested parameters and no output schema, so the description must explain returns and behavior. It does so by defining the four result classes and cap behavior, while annotations and the rich schema cover safety and parameter details.

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

Parameters4/5

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

Schema description coverage is 100%, so the baseline is 3. The description still adds meaning by stating that predicate names come from law_packages and by explaining the limits.maxArguments cap and how to respond to a refusal.

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 clearly states that the tool measures how robust a conclusion is under an argumentation profile and enumerates four result classes. It distinguishes the operation from ordinary querying by focusing on argumentation, but it does not explicitly name or contrast sibling tools.

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?

Usage is implied by 'How robust a conclusion is', and the description gives operational guidance about predicate names and cap handling. However, it does not explicitly say when to use this tool instead of siblings such as law_ask, law_query, or law_explain.

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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