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by io.lexq.mcp
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Adjust A/B Test

lexq_ab_test_adjust
Destructive

Adjust traffic rate of a running A/B test.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
groupIdYesPolicy group ID
trafficRateYesNew traffic percentage for challenger (1-99)

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed3 schema fields changed
    • changedInput schema / $schema
      Previous value: -"http://json-schema.org/draft-07/schema#"New value: +"https://json-schema.org/draft/2020-12/schema"
    • removedInput schema / additionalProperties
      Removed value: -false
    • addedInput schema / properties / groupId / pattern
      Added value: +"^([0-9a-fA-F]{8}-[0-9a-fA-F]{4}-[1-8][0-9a-fA-F]{3}-[89abAB][0-9a-fA-F]{3}-[0-9a-fA-F]{12}|00000000-0000-0000-0000-000000000000|ffffffff-ffff-ffff-ffff-ffffffffffff)$"
  2. Changed4 schema fields changed
    • removedInput schema / properties / challengerWeight
      Removed value: -{
-  "description": "New challenger weight (%)",
-  "maximum": 99,
-  "minimum": 1,
-  "type": "integer"
-}
    • removedInput schema / properties / controlWeight
      Removed value: -{
-  "description": "New control weight (%)",
-  "maximum": 99,
-  "minimum": 1,
-  "type": "integer"
-}
    • addedInput schema / properties / trafficRate
      Added value: +{
+  "description": "New traffic percentage for challenger (1-99)",
+  "maximum": 99,
+  "minimum": 1,
+  "type": "integer"
+}
    • changedInput schema / required
      Previous value: -[
-  "groupId",
-  "controlWeight",
-  "challengerWeight"
-]New value: +[
+  "groupId",
+  "trafficRate"
+]
  3. First observed

TDQS

A4/5.0
Behavior3/5

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

Annotations already declare destructiveHint=true and readOnlyHint=false, so the mutation risk is known. The description adds the precondition that the test must be running, but it does not go deeper into side effects or irreversibility beyond what annotations already signal.

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?

One short, front-loaded sentence with no filler. It states the action and target resource immediately, making it easy to parse.

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?

For a two-parameter tool with complete schema descriptions and annotations covering destructiveness, the description provides the key missing context: the test must already be running. No output schema exists, but that is not a major gap for this simple adjustment operation.

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 both groupId and trafficRate are already documented in the input schema. The description's phrase 'traffic rate' aligns with the trafficRate parameter but adds no 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 ('Adjust') and resource ('traffic rate of a running A/B test'), making the operation unambiguous. It also differentiates from sibling tools like lexq_ab_test_start and lexq_ab_test_stop, since adjusting is neither starting nor stopping.

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 makes clear that the tool applies to a 'running' A/B test, which is a useful usage condition. It does not explicitly name alternatives or exclusion cases, but the context is clear enough for an agent to select it over start/stop siblings.

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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