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Metrx MCP Server

by metrxbots

Apply Optimization

metrx_apply_optimization
Idempotent

Apply one-click optimization recommendations to an agent for validated changes such as token guardrails or model switches. Use only for suggestions marked as one-click to ensure safe updates.

Instructions

Apply a one-click optimization recommendation to an agent. Only works for suggestions marked as "one_click: true". Common optimizations include setting max_tokens limits and switching models. Do NOT use for unvalidated changes — run create_model_experiment first if unsure about impact.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
agent_idYesThe agent to apply the optimization to
optimization_typeYesThe type of optimization to apply (e.g., "token_guardrails", "model_switch")
payloadNoOverride the default optimization payload (advanced)
Behavior5/5

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

Annotations are consistent (idempotentHint=true, readOnlyHint=false). Description adds critical behavioral context: the tool only applies one-click recommendations and should not be used for unvalidated changes. 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: purpose, condition, and usage guidance. No filler, front-loaded with essential information.

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?

Given the tool's simplicity (3 params, no output schema), the description fully covers what it does, when it works, and when to use alternatives. No missing crucial details.

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 baseline is 3. The description provides examples of optimizations (max_tokens, model switch) but does not add detail beyond schema descriptions for the parameters themselves.

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 'Apply' and the resource 'optimization recommendation to an agent'. It specifies the condition 'one_click: true', distinguishing it from other tools.

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 warns 'Do NOT use for unvalidated changes' and recommends an alternative tool 'create_model_experiment' for uncertain cases. Also states that it only works for one_click suggestions.

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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