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plausible-analytics

by io.usefulapi

Aggregate totals

plausible_aggregate
Read-only

Convenience: aggregate totals for the given metrics over a date range (no group-by). Plausible: POST /api/v2/query with no dimensions.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
filtersNoOptional filters. Each is an array like ["is","visit:country_name",["Estonia"]] or ["contains","event:page",["/blog"]] with logical wrappers ["and",[...]] / ["or",[...]] / ["not",[...]]. Operators: is, is_not, contains, contains_not, matches, matches_not, has_done, has_not_done.
metricsYesMetrics to compute. Any of: visitors, visits, pageviews, views_per_visit, bounce_rate, visit_duration, events, scroll_depth, percentage, conversion_rate, group_conversion_rate, average_revenue, total_revenue, time_on_page.
site_idYesThe site's domain as registered in Plausible, e.g. "example.com".
date_rangeYesDate range: a shortcut string ("day", "7d", "28d", "30d", "91d", "month", "6mo", "12mo", "year", "all", "24h") OR a 2-element ISO8601 array like ["2024-01-01","2024-07-01"] (dates or datetimes with tz).

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

A4/5.0
Behavior3/5

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

The annotation readOnlyHint=true already establishes it as a safe read operation. The description adds the underlying API call (POST /api/v2/query with no dimensions), which is useful context, but it doesn't disclose additional behavioral aspects like rate limits or response format. Given the annotation coverage, a moderate score is appropriate.

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 two concise sentences, front-loaded with the key purpose ('aggregate totals') and efficiently adds the API implementation detail. Every phrase earns its place without redundancy.

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?

For a simple aggregate tool with strong schema coverage and a read-only annotation, the description is largely complete. It clearly defines the operation and contrasts with breakdown. However, since there is no output schema, the description doesn't specify the return shape (e.g., a single row of totals), which is a minor gap for agent expectations.

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?

The schema has 100% description coverage with detailed explanations for site_id, metrics, date_range, and filters. The description adds minimal parameter insight beyond the schema, only clarifying that there is no group-by dimension. Therefore, the schema carries the burden and a baseline of 3 is warranted.

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 'aggregate totals for the given metrics over a date range (no group-by)' which specifies the verb, resource, and scope. It also differentiates from sibling tools like plausible_breakdown (which would involve group-by) and plausible_timeseries, making it uniquely identifiable.

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 provides clear context: it's a convenience tool for total aggregates without group-by. It doesn't explicitly name alternatives, but the 'no group-by' phrasing signals when to prefer this over breakdown or timeseries tools. No explicit exclusions are mentioned, hence a 4.

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

All plausible-analytics toolsInspect
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TDQS

A3.7/5.0
Disambiguation4/5

Tools are mostly distinct: aggregate, breakdown, timeseries, and realtime_visitors each serve a specific query pattern, while query_stats is the general-purpose fallback. Minor overlap exists because query_stats can reproduce what the convenience wrappers do, but descriptions clarify the intended use.

Naming Consistency4/5

All tools share the plausible_ prefix and use lowercase with underscores, which is consistent. However, some names are single verbs/nouns (aggregate, breakdown, timeseries) rather than a uniform verb_noun pattern like list_sites, so there's a slight stylistic inconsistency.

Tool Count5/5

8 tools is a well-scoped size for an analytics server. It covers the main query types (totals, breakdowns, time series, realtime) plus site and goal listing, without being bloated or insufficient.

Completeness4/5

The server covers the core analytics surface: querying metrics with aggregation, breakdown, timeseries, and realtime, plus site and goal metadata. Minor gaps exist like goal creation or site management, but these are likely outside the intended read-only analytics scope.

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