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Read plan usage

read_plan_usage
Read-only

Reads the account's plan and what it has used of each monthly quota: leads captured, emails sent, AI messages, plus how many websites and widgets exist against the plan's limits. Leads are counted over the billing period and everything else over the calendar month (UTC). plan is null when no plan is active. Answers "what is my limit" and "how close am I to the cap". Changes nothing.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.1/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=true and destructiveHint=false, so "Changes nothing" is largely redundant. The description does add genuinely non-obvious behavior: leads are counted over the billing period while everything else uses the UTC calendar month, and `plan` is null when no plan is active — real semantic detail an agent could not infer. With the safety profile already covered by annotations, a 3 reflects useful-but-partial added context.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Front-loaded with the verb and the enumerated metrics, then the counting-period caveat, then the null case. Every clause carries information, though the sentence listing quotas could be tightened slightly.

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?

With no output schema, the description must convey what comes back, and it does: per-quota usage versus plan limits, plan and website/widget counts, plus the null-plan edge case. It does not describe the shape of the returned object or how usage is expressed (counts versus percentages), leaving a minor gap.

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

Parameters4/5

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

The tool takes zero parameters, so the baseline is 4; the description correctly implies no filtering scope is needed. It does note that the response's `plan` field may be null, which is a small but useful addition about returned semantics.

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?

States a specific verb and resource (the account's plan plus per-quota usage) and enumerates exactly which quantities are reported: leads captured, emails sent, AI messages, websites, widgets. This is clearly distinct from every sibling, which are per-object reads like read_lead or read_analytics.

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?

Frames the use case explicitly with the two questions it answers ("what is my limit" and "how close am I to the cap"), which tells an agent when to reach for it. It stops short of naming an alternative tool or a when-not condition, so it is clear context rather than full routing guidance.

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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