LexQ

Dry Run

lexq_dry_run

Read-only

Execute a single dry run against a version. Tests how rules evaluate given input facts without side effects.

Returns: inputFacts — normalized input facts mutatedFacts — input facts changed by rule actions (e.g. MUTATE_FACT mutates paymentAmount) generatedVariables — system-generated values; every fact in mutatedFacts gets a paired {factName}__delta key (signed difference) executionTraces — per-rule match status decisionTraces — per-rule decision (SELECTED / NO_MATCH / BLOCKED / etc.)

Example input: { "facts": { "paymentAmount": 100000, "customerTier": "VIP" } } Always dry-run before publishing to validate rule behavior.

Input Schema

TableJSON Schema

Name	Required	Description
`facts`	Yes	JSON string of facts object, e.g. {"paymentAmount":100000}
`versionId`	Yes	Policy version ID to test against
`includeDebugInfo`	No	Include execution and decision traces

Schema Changelog

Changes observed during successful MCP inspections.

Changed5 schema fields changedSep 10, 2026

changedInput schema / $schema

Previous value: -"http://json-schema.org/draft-07/schema#"New value: +"https://json-schema.org/draft/2020-12/schema"

removedInput schema / additionalProperties
```
Removed value: -false
```

changedInput schema / properties / facts / description

Previous value: -"JSON string of facts object, e.g. {\"payment_amount\":100000}"New value: +"JSON string of facts object, e.g. {\"paymentAmount\":100000}"

removedInput schema / properties / mockExternalCalls

Removed value: -{
-  "default": true,
-  "description": "Mock external integration calls",
-  "type": "boolean"
-}

addedInput schema / properties / versionId / pattern

Added value: +"^([0-9a-fA-F]{8}-[0-9a-fA-F]{4}-[1-8][0-9a-fA-F]{3}-[89abAB][0-9a-fA-F]{3}-[0-9a-fA-F]{12}|00000000-0000-0000-0000-000000000000|ffffffff-ffff-ffff-ffff-ffffffffffff)$"

First observedApr 2, 2026

TDQS

A4.5/5.0

Behavior5/5

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

Even with readOnlyHint=true already set, the description adds valuable behavioral context: 'without side effects,' normalized outputs, mutatedFacts delta keys, and per-rule traces. This goes well beyond the annotation and helps an agent understand exactly what a dry run does and returns.

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 front-loaded with the core purpose and uses a clear bulleted return-value breakdown plus an example input. Every section earns its place; it is detailed without being bloated.

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

Completeness5/5

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

With no output schema, the description fully compensates by enumerating all return fields and their meanings. It also provides an example and usage guidance, making the tool callable correctly with minimal inference.

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 description coverage is 100%, so the schema already documents facts, versionId, and includeDebugInfo. The description contributes an example input and explains return behavior, but does not add much parameter-level meaning beyond the schema.

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 states a specific verb and resource: 'Execute a single dry run against a version.' It clearly explains what the tool does and implicitly distinguishes itself from lexq_dry_run_compare by focusing on a single execution rather than comparison.

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 gives explicit when-to-use guidance: 'Always dry-run before publishing to validate rule behavior.' It does not name alternative tools or state when not to use it, but the single-run framing provides enough context to separate it from compare and simulation tools.

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

All LexQ tools Inspect

Try in Browser

One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

Resources

Need Help?