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CausalLayer MCP

by io.github.smq9sn5jck-coder

simulate_remediation

Counterfactual remediation simulator. Given a certificate's verdict + fourFactorScoring + agents and a list of remediation IDs from the FK-METHOD-2026-003 catalog, return the apportioned shares each remediation would have produced (in isolation) and the composite shares if they all stack. Every remediation cites a specific statute or standard. GET /api/v2/remediation/catalog for the list of IDs. Cost: 1 credit (same price as verify_certificate). Pure deterministic; same inputs produce a byte-identical result.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
agentsYesAgent registry (id + type) so the simulator can map remediation targetType to specific party ids.
verdictYesThe verdict block from the CausalCertificate.
remediationsYesList of remediation IDs from the catalog (e.g. vendor_adversarial_eval_suite, deployer_human_in_loop). Each may optionally pin appliedToParty to a specific agent id.
fourFactorScoringYesThe fourFactorScoring block from the CausalCertificate.

TDQS

A4.4/5.0
Behavior4/5

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

With no annotations, the description carries full burden. It discloses determinism (byte-identical results), cost (1 credit), and implicitly indicates it is a read-only operation via the GET method mention. It does not discuss authentication or rate limits, but the provided details are substantial for safe invocation.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise and front-loaded. The first sentence summarizes the tool's purpose and inputs. Every sentence adds value: inputs, outputs, reference to catalog, cost, and determinism. There is no redundant or unnecessary text.

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 (4 parameters with nested objects, no output schema), the description is fairly complete. It covers what inputs are needed, what outputs are returned, and provides additional context like cost, determinism, and catalog reference. The lack of output schema is compensated by describing the output in words.

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 the schema already defines parameters. The description adds context by stating that parameters come from a CausalCertificate, that remediations are from the FK-METHOD-2026-003 catalog, and that each remediation can optionally pin an appliedToParty. This adds meaning beyond the raw schema definitions.

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 identifies the tool as a counterfactual remediation simulator, specifying the inputs (verdict, fourFactorScoring, agents, remediation IDs) and outputs (apportioned shares, composite shares). It references the catalog endpoint for obtaining remediation IDs, distinguishing it from sibling tools like verify_certificate.

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 explicitly states when to use the tool: given a certificate's verdict, fourFactorScoring, agents, and remediation IDs. It mentions the cost and deterministic behavior, and references the catalog endpoint for obtaining valid IDs. It does not explicitly exclude scenarios, but the context is clear enough for appropriate usage.

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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