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Plausible Analytics MCP Server

by go-ai-now

Plausible: timeseries

plausible_get_timeseries
Read-onlyIdempotent

Get website metrics bucketed by hour, day, week, or month to identify traffic trends, spikes, and drops over any date range.

Instructions

Get metrics bucketed over time (hour/day/week/month) to see trends, spikes and drops.

Examples:

  • Daily visitors for the last 30 days: {"site_id": "example.com", "date_range": "30d", "interval": "day"}

  • Hourly traffic today from one country: {"date_range": "day", "interval": "hour", "filters": [{"dimension": "visit:country_name", "operator": "is", "values": ["Germany"]}]}

  • Monthly signups this year: {"date_range": "year", "interval": "month", "metrics": ["visitors", "events"], "filters": [{"dimension": "event:goal", "operator": "is", "values": ["Signup"]}]}

Dates/times are in the site's reporting timezone. Hourly buckets over long ranges produce many rows; prefer day/week for ranges over a few days.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
filtersNoFilters combined with logical AND. Each filter matches if any of its values match.
metricsNoMetrics per bucket. Default: visitors, pageviews
site_idNoSite domain exactly as registered in Plausible, e.g. "example.com". Optional when PLAUSIBLE_DEFAULT_SITE_ID is set.
intervalNoBucket size. "auto" lets Plausible pick one that suits the date rangeauto
fill_gapsNoReturn every bucket in the range, including empty ones (counts become 0, ratios null)
date_rangeNoEither a preset ("day", "24h", "7d", "28d", "30d", "91d", "month", "6mo", "12mo", "year", "all") or a custom range {"from": "2024-01-01", "to": "2024-01-31"}30d
response_formatNo"markdown" (default) for a readable table, "json" for machine-readable rowsmarkdown
include_importedNoInclude imported (e.g. Google Analytics) data where supported

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.5/5.0
Behavior4/5

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

Annotations already cover read-only and idempotent behavior. The description adds useful behavioral context beyond that: dates/times use the site's reporting timezone, and hourly buckets over long ranges can produce many rows. No contradictions with annotations.

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 compact and front-loaded: one purpose sentence, three useful JSON examples, and one operational caveat. No filler or redundancy.

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?

For a tool with 8 parameters, a rich schema, and safe annotations, the description covers the key decision points: range, interval, filters, metrics, and output-size trade-offs. The schema handles the remaining constraints appropriately.

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?

Schema coverage is 100%, so the baseline is 3. The description adds practical meaning through complete example payloads showing how filters, date_range, interval, and metrics fit together, plus the guidance to prefer day/week for long ranges.

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: 'Get metrics bucketed over time (hour/day/week/month)'. This clearly differentiates it from sibling tools like aggregate, breakdown, and realtime by its time-series nature.

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?

Provides concrete usage scenarios via examples (daily visitors, hourly traffic from a country, monthly signups) and an operational warning about long hourly ranges. However, it never explicitly names sibling alternatives or states when to use aggregate/breakdown instead.

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