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Plausible Analytics MCP Server

Plausible Analytics MCP Server

by AryanKo
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

query_stats

Query Plausible Analytics for website metrics (visitors, pageviews, conversions) by site and date range, using filters, dimensions, and custom ordering to get the exact data you need.

Instructions

Execute a general-purpose analytics query against Plausible Analytics API v2.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "site_id": "<site_id>",
      "metrics": "<metrics>",
      "date_range": "<date_range>"
    },
    "name": "query_stats"
  }
}

Input Schema

NameRequiredDescriptionDefault
filtersNo
metricsYes
site_idYes
order_byNo
date_rangeYes
dimensionsNo
paginationNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

C2.4/5.0
Behavior2/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It only says 'execute a query', which implies a read operation but does not state whether it is read-only, whether authentication is needed, or any side effects. It adds little beyond what the tool name already implies.

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?

The description is a single, front-loaded sentence with no wasted words. It is easy to parse, though it is also under-specified. The conciseness is good but not exceptional because it sacrifices needed detail for brevity.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness1/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (7 parameters, no output schema, no annotations), the description is severely incomplete. It provides no information about return values, pagination, error behavior, or how to construct queries. An agent would not know what to expect from the tool's output or how to use it effectively.

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

Parameters1/5

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

Schema description coverage is 0%, and the description does not compensate by explaining any parameters. It mentions none of the seven parameters, leaving the agent to rely entirely on the schema. The schema itself has minimal descriptions for the main properties, so the tool provides insufficient parameter guidance.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description 'Execute a general-purpose analytics query against Plausible Analytics API v2' clearly states the action (execute) and resource (analytics query). It provides a general sense of scope with 'general-purpose', but it does not explicitly differentiate from sibling tools like get_realtime_visitors or list_sites, so it lacks a strong distinguishing element.

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

Usage Guidelines2/5

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

The description offers no guidance on when to use this tool vs alternatives. There is no mention of specific use cases, prerequisites, or exclusions. Sibling tools are not referenced, so an agent receives no context for choosing query_stats over get_realtime_visitors or list_sites.

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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