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CausalLayer MCP

by io.github.smq9sn5jck-coder

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

A4.6/5.0
Behavior5/5

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

With no annotations, the description fully discloses key behaviors: it is deterministic, costs 1 credit, renormalizes the apportionment sum, and indicates stub status for US/UK/CA. It also mentions the output structure (shares, rules, citations, matrix).

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 information-dense but front-loaded with the core purpose. It covers inputs, outputs, limitations, and usage hints in a single paragraph without redundancy. Could be slightly more structured, but efficient.

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 complexity of 5 parameters (3 required), nested objects, and no output schema, the description adequately explains the tool's purpose, inputs, outputs, behavioral traits (deterministic, credit cost, stub status), and how to check support. It leaves little ambiguity for an agent.

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 value by explaining the overall input semantics (e.g., 'union of jurisdiction role tags', 'union of flags') and describing the output, which is not in the schema. It also hints at flag semantics with examples.

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 explicitly states the function: given an apportionment, actor tags, and flags, it returns post-overlay shares for up to five jurisdictions with triggered rules, citations, and a matrix. It distinguishes itself from sibling tools by its specific jurisdiction overlay purpose.

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 advises using GET /api/v2/jurisdiction/catalog to discover support and stub status, and notes that only AU and EU are fully implemented while others are stubs. This provides context on when to use the tool but does not explicitly exclude alternatives.

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
Disambiguation5/5

Each tool targets a distinct operation: prospective gate, incident extraction, anchor status, issuer registry, jurisdiction overlay, remediation simulation, incident submission (two variants), and certificate verification (two variants). Despite two submission and two verification tools, their descriptions clearly differentiate the inputs and purposes, preventing ambiguity.

Naming Consistency5/5

All tool names follow a consistent verb_noun pattern in snake_case (e.g., evaluate_prospective_response, submit_incident, verify_certificate). The verbs are descriptive and the nouns correspond to the domain objects, making the naming predictable and clear.

Tool Count5/5

With 10 tools, the server covers a complex domain (causal liability attribution for AI incidents) without being overwhelming. Each tool serves a distinct role in the workflow, and the count feels well-scoped for the functionality offered.

Completeness4/5

The tool set covers the core lifecycle: extraction, submission (structured and trace-based), verification (standard and recompute), a prospective gate, jurisdiction query, remediation simulation, and infrastructure queries (anchor, registry). Minor gaps exist, such as no tool to list or search past incidents/certificates, but the essential operations are present.

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