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CausalLayer MCP

by io.github.smq9sn5jck-coder

submit_otel_trace

Convert an OpenTelemetry OTLP JSON trace into a FaultKey incident and return the same deterministic CausalCertificate as submit_incident. Each span becomes an event; service.name groups spans into agents; W3C trace_id and span_id propagate as evidence pointers on the causal graph edges. Cost: 50 credits (same as submit_incident). Three guardrails apply: PII scan, deterministic-only acknowledgement, and minimum evidence (auto-satisfied when the trace has at least 1 span).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
otlpYesOTLP JSON payload with resourceSpans[]. See https://opentelemetry.io/docs/specs/otlp/#json-protobuf-encoding
titleYes
categoryNo
currencyNo
jurisdictionNo
pii_acknowledgedNoG1: Set to true ONLY if PII handling is permitted by your data agreement. OTLP traces frequently leak user/session ids in attributes.
deterministic_onlyYesG2: Must be true. Acknowledges CausalLayer is deterministic.
financial_impact_centsNo

TDQS

A4.4/5.0
Behavior5/5

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

With no annotations, description fully discloses cost (50 credits), three guardrails (PII scan, deterministic-only, minimum evidence), and behavioral details (spans to events, service.name grouping, trace_id/span_id propagation). No contradictions.

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?

Compact 4-sentence description, front-loaded with main action. Every sentence adds unique value: conversion, mapping, cost, guardrails. No redundancy.

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?

Covers main behavior, guardrails, and parameter mapping. Lacks explanation of optional parameters and explicit output details, but mentions returning same CausalCertificate as sibling. Adequate for the complexity.

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 low (38%). Description adds meaning for otlp (OTLP JSON payload with resourceSpans), pii_acknowledged (G1), deterministic_only (G2). However, category, currency, jurisdiction, financial_impact_cents remain unexplained.

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?

Specific verb 'Convert' with resource 'OpenTelemetry OTLP JSON trace' and target 'FaultKey incident'. Clearly distinguishes from sibling submit_incident by input format and mentions deterministic CausalCertificate.

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?

States when to use: for converting OTLP traces. Implicitly contrasts with submit_incident. Does not explicitly list when not to use, but context is clear.

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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