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UN Charter 1945

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)
contextNoExplicit temporal context: timezone is an IANA name; decisionTime and knowledgeTime are complete Instants with UTC or an offset, preserved unchanged. legalTime is also required with context; a profile may require all these fields
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": "Explicit temporal context: timezone is an IANA name; decisionTime and knowledgeTime are complete Instants with UTC or an offset, preserved unchanged. legalTime is also required with context; a profile may require all these fields",
+  "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

A3.6/5.0
Behavior4/5

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

The annotations already mark this as read-only, non-destructive, and closed-world, so safety is covered. The description adds meaningful behavioral context beyond annotations: it explains the four output classes, that all arguments are built including chains that did not fire, and that a cap-based refusal is possible with suggested mitigation.

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 dense but front-loads the core purpose and acceptance classes before detailing argument construction and cap behavior. Every sentence carries useful information, though the first phrase is slightly awkward and the run-on structure could be tightened.

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?

For a complex tool with seven parameters, nested objects, and no output schema, the description does a good job explaining the return classes and key behavioral caveats. It leaves some minor gaps around the exact response shape and the interaction of legalTime with context, but overall it gives an agent enough to invoke the tool correctly.

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 schema already documents parameters thoroughly. The description still adds useful semantic context: predicate names come from law_packages, facts have a provenance requirement handled by EvidencePolicy, legalTime is conditionally required with context, and limits control argument caps.

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 the four possible acceptance classes. It is specific enough to distinguish this from a simple query or explanation, though it does not explicitly contrast itself with sibling tools such as law_ask or law_explain.

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?

The description explains what the tool computes but gives almost no guidance on when to choose it over alternatives. It only notes where predicate names come from and a cap-related workaround, without stating the conditions under which an agent should invoke law_argue instead of 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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