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Neuratel-AI

Neuratel MCP Server

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by Neuratel-AI

get_usage

Get usage summary for a time period: call count, total seconds/minutes, and billed amount. Use for cost monitoring, capacity planning, and usage reporting by specifying look-back days.

Instructions

Get usage summary for a time period.

Returns aggregate stats: how many calls were made, total seconds and minutes consumed, and total amount billed. Billing is per-second, so total_seconds is the granular metric. Useful for cost monitoring, capacity planning, and usage reporting.

Args: days: Look-back period in days (default 30, max 365). Use 1 for today's usage, 7 for the past week.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
daysNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior4/5

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

With no annotations, the description carries the behavioral transparency burden. It discloses important nuances such as 'Billing is per-second, so total_seconds is the granular metric' and explains the days parameter's limits and default. It does not explicitly state read-only behavior or error handling, but those are largely implied by the 'Get' verb and simple summary nature.

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 lean and well-organized: a one-line purpose, two sentences explaining the returned metrics and a billing nuance, and a clear Args section. Every sentence adds value, with no fluff or repetition.

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?

The tool is simple (one optional parameter) and has an output schema, so the description does not need to detail return fields. It covers purpose, use cases, and parameter semantics, making it fully adequate for the tool's complexity.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema only provides a bare integer with a default for 'days', while the description adds full meaning: it is a look-back period, has a maximum (365), and gives practical examples ('Use 1 for today's usage, 7 for the past week'). This completely compensates for the 0% schema description coverage.

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 opens with 'Get usage summary for a time period' and explicitly lists what the summary contains ('how many calls were made, total seconds and minutes consumed, and total amount billed'). This clearly identifies the resource and distinguishes it from siblings like get_balance or get_active_calls.

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 states concrete use cases ('cost monitoring, capacity planning, and usage reporting'), giving clear guidance on when to use the tool. However, it does not explicitly mention alternative tools or when not to use it, so it stops short of a full 5.

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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