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FaultKey · CausalLayer

by io.github.smq9sn5jck-cloud

query_jurisdiction_overlay

Multi-jurisdiction overlay (FK-METHOD-2026-004). Given a canonical attributable apportionment (party-id -> share), the union of all jurisdiction role tags on each actor, and the union of jurisdiction-specific flags, return side-by-side post-overlay shares for AU, EU, US, UK, CA (or a chosen subset) with the specific rules that fired in each, citation URLs, and a parties × jurisdictions matrix. v1 ships full implementations for AU and EU; US/UK/CA are research stubs marked is_stub: true. Use GET /api/v2/jurisdiction/catalog to discover support and stub status. Cost: 1 credit. Pure deterministic.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
flagsYesUnion of jurisdiction-specific flags. AI Act / PLD flags drive the EU overlay; ACL / CPS 230 / VAISS flags drive the AU overlay.
actorsYesAll actors with the union of jurisdiction-specific role tags. EU and AU tags coexist on the same actor record.
attributableYesCanonical pre-overlay apportionment as { party_id: share }. Sum should approximate 1.0; the function renormalises within tolerance.
jurisdictionsNoOptional subset to compute. Defaults to all five.
primaryJurisdictionNoEngine-level jurisdiction string (e.g. 'EU', 'DE', 'AU'). Used by the EU gate to decide engagement.

TDQS

A4.5/5.0
Behavior5/5

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

With no annotations provided, the description carries the full burden. It discloses key behavioral traits: full implementations for AU/EU, research stubs for US/UK/CA with `is_stub: true`, pure determinism, credit cost, and the need to consult a catalog for support. It also describes the return format. This is exemplary transparency.

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 appropriately sized for a complex tool. It front-loads the core purpose, then adds stub status, discovery, cost, and determinism in separate clauses. Every sentence earns its place, though it is slightly long.

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?

Given the tool's complexity (5 nested parameters, no output schema), the description adequately covers return values, stub limitations, and a discovery mechanism. It could mention error handling or the role of primaryJurisdiction, but the schema covers the latter. Overall it is sufficiently complete for an agent to invoke 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 coverage is 100%, so baseline is 3. The description adds conceptual meaning by framing the three required parameters ('attributable apportionment', 'union of jurisdiction role tags', 'union of jurisdiction-specific flags') and notes optional subset selection. This adds value beyond the schema's individual field descriptions.

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's purpose with a specific verb ('return') and resource ('multi-jurisdiction overlay'), enumerating the output components (post-overlay shares, rules fired, citation URLs, matrix). It distinguishes itself from siblings by specifying its unique computation and jurisdictions.

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 context on when to use the tool (given a canonical apportionment, actor tags, and flags) and includes a clear pre-flight instruction to check the catalog for stub status. It does not explicitly name alternative tools, but the context is sufficient for correct selection.

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

A4.2/5.0
Disambiguation4/5

Most tools have distinct purposes, but submit_incident and submit_otel_trace both create certificates from different input sources, and verify_certificate and verify_certificate_recompute both verify but via different methods. Descriptions and input types make them distinguishable, so ambiguity is low.

Naming Consistency4/5

All names use snake_case and generally follow a verb_noun pattern (e.g., submit_incident, verify_certificate). Deviations like verify_certificate_recompute (verb_noun_modifier) and evaluate_prospective_response (verb_adjective_noun) are minor and remain predictable.

Tool Count5/5

10 tools is well within the ideal 3-15 range and maps cleanly to the domain: incident submission, text extraction, pre-response gating, certificate verification, remediation simulation, jurisdiction analysis, and issuer/anchor metadata queries. Each tool serves a distinct workflow step without redundancy.

Completeness4/5

The core lifecycle (extract → submit → verify → analyze) is well covered. However, several tools reference external catalog endpoints (e.g., /jurisdiction/catalog, /remediation/catalog, /gate/thresholds) that are not exposed as MCP tools, forcing an agent to make separate HTTP calls to use these tools effectively. This is a minor workaround, not a critical gap.

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