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Metrx MCP Server

by metrxbots

Create Model Experiment

metrx_create_model_experiment

Run A/B experiments comparing LLM models for an agent. Route traffic to a candidate model, track cost, latency, error rate, and quality until statistical significance is reached.

Instructions

Start an A/B test comparing two LLM models for a specific agent. Routes a percentage of traffic to the treatment model and tracks cost, latency, error rate, and quality metrics. The experiment runs until statistical significance is reached or the max duration expires. Do NOT use for one-off model comparisons — use compare_models for static pricing data.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
agent_idYesAgent to run the experiment on
nameYesHuman-readable experiment name
treatment_modelYesThe candidate model to test (e.g., "gpt-4o-mini", "claude-haiku-4-20250414")
traffic_pctNoPercentage of traffic to route to the treatment model (default: 10%)
primary_metricNoThe primary metric to optimize for (default: cost_per_call)cost_per_call
max_duration_daysNoMaximum experiment duration in days (default: 14)
auto_promoteNoAutomatically apply the winning model when the experiment completes
Behavior4/5

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

The description discloses key behaviors: routing traffic, tracking metrics, running until significance or max duration, and auto-promote option. Annotations provide no behavioral hints, so the description carries the full burden. It is thorough but could mention what happens if not auto-promoted or how to stop. No contradiction with annotations.

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 three sentences with no wasted words. It front-loads the main purpose, then explains mechanics, and ends with usage guidelines. Well-structured and concise.

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?

Given the complexity (7 params, no output schema), the description covers purpose, behavior, and sibling distinction. It lacks mention of the return value (like experiment ID) and prerequisites (e.g., agent must exist). Still, it is fairly complete for an AI agent.

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?

The input schema has 100% description coverage for all 7 parameters. The tool description summarizes the parameters' roles but does not add significant new meaning beyond the schema. Baseline 3 is appropriate.

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: 'Start an A/B test comparing two LLM models for a specific agent.' It also distinguishes from a sibling tool by explicitly saying 'Do NOT use for one-off model comparisons — use compare_models for static pricing data.'

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?

The description provides explicit when-to-use and when-not-to-use guidance, including an alternative tool for one-off comparisons. It says 'Routes a percentage of traffic...' and 'Do NOT use for one-off model comparisons — use compare_models for static pricing data.'

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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