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engineering_sensor_anomaly_detection

Destructive

Detect anomalies in engineering sensor data by submitting a free-text objective and optional JSON inputs. Routes through the Lightbulb domain-agent dispatcher for analysis.

Instructions

Run the engineering domain agent action sensor_anomaly_detection.

Routes through the platform's domain-agent dispatcher under your JWT, tenant, and company scope.

Args: message: Free-text objective for the action. inputs: Optional JSON string of structured inputs for the action.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
inputsNo{}
messageNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.5/5.0
Behavior3/5

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

The description mentions routing through the platform's domain-agent dispatcher and scoping under JWT, tenant, and company, which gives some behavioral context. However, it does not explicitly describe side effects, permissions, or consequences of execution, and relies on annotations for destructive intent.

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 well-structured, with a clear statement of the action, routing details, and two parameter explanations. No unnecessary information is included, making it easy to read and understand quickly.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description covers the tool's purpose and parameters but omits any mention of output or return values, which are defined in the output schema but not described. This leaves the agent without expectations for what a successful call returns, reducing completeness.

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?

Although the schema has no parameter descriptions (0% coverage), the description compensates partially by explaining 'message' as 'Free-text objective' and 'inputs' as 'Optional JSON string of structured inputs'. This is minimal but provides some semantics. Given both parameters are optional, the description is sufficient but not detailed.

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: to run the engineering domain agent action `sensor_anomaly_detection`. It distinguishes itself by naming the specific action and the routing mechanism, making it easily identifiable among many sibling tools.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no explicit guidance on when to use this tool versus alternatives. It does not compare with other engineering or anomaly-related tools, leaving the agent to infer applicability from the name alone.

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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