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Glama

plausible-analytics

by io.usefulapi

Breakdown by property

plausible_breakdown
Read-only

Convenience: break metrics down by a single property (dimension), e.g. visit:source or event:page. Plausible: POST /api/v2/query with dimensions=[property].

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMax rows to return — maps to pagination.limit.
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".
order_byNoOptional ordering: an array of [metric_or_dimension, "asc"|"desc"] pairs, e.g. [["visitors","desc"]].
propertyYesThe single dimension to group by, e.g. visit:source, event:page, visit:country_name, visit:device.
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?

Annotations already declare readOnlyHint=true, so the safe-read behavior is known. The description adds value by revealing the underlying API call (POST /api/v2/query with dimensions=[property]) and the 'convenience' nature, but it does not disclose pagination, response structure, or other behavioral traits. This is adequate but not exceptional.

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, front-loaded with the core purpose, and includes a useful API mapping. Every word earns its place with no repetition of schema details.

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, combined with a rich schema and read-only annotation, gives enough context for an agent to invoke the tool correctly. It explains the core behavior and API equivalence, though it does not describe the return format or edge cases. For a convenience wrapper this is moderately complete.

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 coverage is 100%, so the schema already documents all 7 parameters. The description reiterates the property examples already present in the schema and does not add new semantic meaning beyond pointing out the mapping to the API. 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 clearly states a specific action: "break metrics down by a single property (dimension)" with concrete examples like "visit:source or event:page". It distinguishes itself from sibling tools like timeseries (which breaks down by time) and aggregate (which does not break down).

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 by labeling itself as a convenience wrapper for the Plausible API and specifying the exact API call. It implies when to use it (when you need breakdown by a single property) but does not explicitly name alternatives or exclusion cases, so it misses the top score.

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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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.

Resources