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CausalLayer MCP

by io.github.smq9sn5jck-coder

extract_incident

Claude-powered structured extractor. Parses unstructured text (news articles, court filings, emails, PDFs, incident reports, logs) into the typed JSON schema required by submit_incident. Returns a ready-to-submit incident object with extracted agents, events, severity, jurisdiction, and financial impact. NOTE: This is a pre-processing convenience tool — the deterministic scoring engine itself remains LLM-free. Cost: 10 credits.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
textYesUnstructured text to extract from. Can be a news article, court filing, incident report, email, PDF text, log output, or any description of an AI incident.
context_hintNoOptional hint about the source type (e.g., 'court filing', 'news article', 'internal incident report') to improve extraction accuracy.
jurisdiction_hintNoOptional ISO country code hint if the jurisdiction is known (e.g., 'AU', 'US', 'EU').

TDQS

A4.2/5.0
Behavior4/5

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

No annotations are provided, so the description carries full burden. It discloses that the tool is Claude-powered, costs 10 credits, returns a ready-to-submit object, and mentions the LLM-free scoring engine. It does not detail error handling or privacy, but the core behavioral traits are transparent.

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?

Three well-structured sentences: first states purpose, second lists inputs and outputs, third adds context and cost. No unnecessary words, efficiently conveys all key information.

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 3 well-described parameters and no output schema, the description covers the tool's role, inputs, outputs, cost, and relationship to sibling tools. It lacks information on error cases or extraction failures, but is adequate for a pre-processing convenience tool.

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

Parameters3/5

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

Schema coverage is 100% with clear parameter descriptions. The tool description adds value by listing output fields (agents, events, severity, jurisdiction, financial impact) but does not elaborate on the parameters beyond what the schema provides. Baseline 3 is appropriate.

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 function: extracting structured incident data from unstructured text for submission via submit_incident. It lists input types (news articles, court filings, etc.) and output components (agents, events, severity, jurisdiction, financial impact), and explicitly distinguishes it from the submit_incident sibling as a pre-processing convenience tool.

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 positions the tool as a pre-processing step before submit_incident ('required by submit_incident', 'returns a ready-to-submit incident object'). It does not explicitly state when not to use it or list alternatives, but the context is clear given the sibling tools.

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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