Skip to main content
Glama
SweetKenneth

nightmare-probe-engine

by SweetKenneth

nightmare_compile_probe

Turn a hypothesis into a bounded, targeted probe plan with optional control cohort, enabling falsifiable testing and clear verdicts.

Instructions

Compile one hypothesis into a bounded targeted probe plan with optional control cohort.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
assetsYes
budgetNo
historyYes
hypothesisYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

C2.5/5.0
Behavior1/5

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

With no annotations and no output schema, the description carries the full burden of explaining behavior. The text only says 'compile' and 'bounded', which does not disclose whether the tool triggers expensive work, has side effects, requires auth, modifies state, or how it treats the given assets and history. The behavioral surface is essentially undisclosed.

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?

A single front-loaded sentence that wastes no words and names the core transformation. The adjectives 'bounded' and 'targeted' add qualifiers without verbosity. It is efficient, though perhaps too terse to be maximally useful.

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

Completeness2/5

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

The tool has 4 parameters (3 required), nested input objects, no output schema, and no annotations. The description explains the core operation but omits what the tool will do with those required parameters, what a 'probe plan' contains, and what expected behavior is. The absent output format and undocumented inputs make this incomplete for safe invocation.

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

Parameters2/5

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

Schema description coverage is 0%, so the description must compensate. It adds bit of meaning by identifying 'hypothesis' as the singular hypothesis and referencing an 'optional control cohort', which likely maps to 'budget'. But it fails to explain 'assets', 'history', or 'budget' meaningfully, leaving most of the four parameters opaque.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly names the action ('Compile one hypothesis into a bounded targeted probe plan') with a specific verb and resource, and the contrast with siblings 'nightmare_generate' and 'nightmare_evaluate_probe' suggests the compile step is distinct. However, it does not explicitly contrast itself with those siblings or define what distinguishes a 'probe plan' from a 'generate' output, so it stops just short of a 5.

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?

No guidance is given about when to use this tool versus the siblings. The phrase 'Compile one hypothesis into a plan' implies a use case, but it never instructs when not to use it or names an alternative. The agent is left to infer the tool's role in the generation/evaluation pipeline.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.