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Metrx MCP Server

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Create Model Experiment

metrx_create_model_experiment

Initiate an A/B test comparing two LLM models for an agent, routing traffic to the treatment model while tracking cost, latency, error rate, and quality until statistical significance or max duration.

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 concludes
Behavior4/5

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

Adds useful behavioral context beyond annotations: experiment runs until statistical significance or max duration, tracks cost/latency/error/quality. Annotations are all false, so no contradiction.

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?

Three sentences with no wasted words: first states purpose, second adds detail, third gives exclusion. Perfectly front-loaded.

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?

Covers experiment behavior, parameters, and sibling distinction well. Missing output description, but no output schema exists, so not a major gap.

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 coverage is 100%, so description adds little new parameter info. Examples for treatment_model and primary_metric provide slight additional context, but baseline 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?

Description clearly states it starts an A/B test comparing two LLM models for a specific agent, routing traffic and tracking metrics. It explicitly distinguishes from sibling 'compare_models' by noting it is not for one-off comparisons.

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?

Provides explicit when-to-use (A/B testing) and when-not-to (one-off comparisons) and names the alternative tool 'compare_models' for static 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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