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Metrx MCP Server

by metrxbots

Generate ROI Audit Report

metrx_generate_roi_audit
Read-onlyIdempotent

Generate a complete ROI audit report for your AI agent fleet, covering per-agent costs, revenue, attribution confidence, and optimization opportunities for board reporting.

Instructions

Generate a comprehensive ROI audit report for your AI agent fleet. Includes per-agent cost/revenue breakdown, attribution confidence scores, optimization opportunities, and risk flags. Suitable for board reporting and compliance. Do NOT use for quick per-agent ROI checks — use get_task_roi for individual agents.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
period_daysNoAnalysis period in days (7-365)
include_methodologyNoInclude methodology notes and caveats for auditors
agent_idsNoSpecific agent IDs to include. Omit for full fleet audit.
Behavior4/5

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

Annotations already indicate readOnlyHint=true, destructiveHint=false, idempotentHint=true. The description adds value by detailing the report contents (per-agent breakdown, confidence scores, risk flags) and suitability for board reporting, which provides context beyond the annotations about the nature of output and use cases.

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 concise with two sentences: the first states the purpose and contents, the second provides explicit usage guidance with a sibling alternative. No extraneous text.

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 annotations cover safety and the schema covers parameters, the description provides sufficient context about the report's purpose and contents. However, it lacks details about the output format (e.g., JSON structure), but the listed contents partially compensate.

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 baseline is 3. The description does not add additional meaning beyond the schema's parameter descriptions (e.g., period_days, include_methodology, agent_ids).

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 it generates a comprehensive ROI audit report for the AI agent fleet, listing specific contents (cost/revenue breakdown, confidence scores, opportunities, risk flags). It distinguishes itself from the sibling tool get_task_roi by explicitly stating when not to use and providing the alternative.

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 explicitly states the appropriate use cases (board reporting, compliance) and provides a clear exclusion: 'Do NOT use for quick per-agent ROI checks — use get_task_roi for individual agents.' This directly guides the agent on tool selection.

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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