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revenuecat-charts-mcp

by stackcurious

rc_compare_periods

Compare a subscription metric between two date ranges, returning averages, totals, deltas, and percentage changes to spot month-over-month or year-over-year trends.

Instructions

Compare a metric across two time periods. Returns averages, totals, deltas, and percentage changes. Useful for month-over-month or year-over-year analysis.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
chart_nameYesThe chart/metric to compare
period1_endYesPeriod A end date (YYYY-MM-DD)
period2_endYesPeriod B end date (YYYY-MM-DD)
period1_startYesPeriod A start date (YYYY-MM-DD)
period2_startYesPeriod B start date (YYYY-MM-DD)

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

A3.8/5.0
Behavior3/5

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

With no annotations, the description bears the full burden of disclosing behavior. It does state the output types (averages, totals, deltas, percentage changes), which is useful, but it does not mention any limitations, date inclusivity rules, error behavior, or side effects. The description provides some transparency but not rich context.

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?

Two sentences with no wasted words. The first sentence states the core action and scope; the second sentence adds output details and use cases. Each sentence earns its place and the key information is 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?

The description is sufficiently complete for a simple comparison tool: it clarifies the operation, the output types, and the primary use cases. Although there is no output schema, the description partially compensates by listing return values. It could additionally clarify period relationships or overlap constraints, but these are minor gaps.

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 schema already documents each parameter, including the chart_name enum and date formats. The description adds no additional meaning beyond the schema, so baseline 3 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?

The description states a specific verb ('Compare') and resource ('a metric across two time periods'), making the tool's function immediately clear. It also names what the tool returns (averages, totals, deltas, percentage changes), distinguishing it from sibling tools like rc_get_chart and rc_get_overview, which do not focus on period-to-period comparison.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The phrase 'Useful for month-over-month or year-over-year analysis' gives an implied use case but does not explicitly state when to prefer this tool over siblings or when not to use it. No exclusions or clear alternative routing are provided, so an agent must infer the usage context.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.