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Rule Test

rule_test
Read-only

Validate security rules by dry-running rule-packs against sample URL, status, headers, and body data to see which rules fire and why, without touching live systems.

Instructions

Dry-run the loaded rule-packs against a sample observation (url/status/headers/body) without touching any host. Use this to check a rule before trusting it, and to understand why one did or did not fire.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlNo
bodyNo
phaseNo
statusNo
headersNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observedv2.1.0

TDQS

A4.3/5.0
Behavior4/5

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

Annotations already provide readOnlyHint=true, and the description adds the 'dry-run' and 'without touching any host' details, reinforcing the safety profile. It also hints at explanatory output ('understand why'), which is useful context beyond the annotation.

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?

Two sentences, front-loaded with the main action and input, and the use case follows. Zero waste, every word earns its place.

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?

With an output schema present, the description need not detail return values. It covers purpose, input composition, and usage scenario. It doesn't mention any prerequisites or edge cases, but given the tool's simplicity and annotations, it is adequately complete.

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 lists url, status, headers, body but omits 'phase' entirely and does not explain the format or purpose of any parameter beyond their names. This is minimal semantics that leaves the agent to infer meaning from names alone.

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 verb 'dry-run' against 'loaded rule-packs' with a specific input (url/status/headers/body). It distinguishes from siblings like rules_list (listing) and rules_reload (reloading) by focusing on testing without host impact.

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

Usage Guidelines5/5

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

Explicitly states when to use: 'before trusting it' and 'to understand why one did or did not fire.' Also includes the key exclusion 'without touching any host,' which tells the agent this is a safe, non-intrusive testing tool.

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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