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Liohtml

Matomo-MCP

by Liohtml

Devices & Technology

matomo_devices
Read-only

Retrieve visitor device and technology breakdowns: device types, brands, browsers, versions, OS, and screen resolutions. Use this data to segment audience and optimize experiences.

Instructions

Devices and technology used by visitors: device types (desktop, mobile, tablet), brands, models, browsers, browser versions, operating systems, and screen resolutions.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
dateNoDate for the report: 'today', 'yesterday', 'YYYY-MM-DD', a rolling window like 'last7' or 'last30', or a range 'YYYY-MM-DD,YYYY-MM-DD' (combine with period=range).yesterday
limitNoMaximum number of rows to return (-1 for all rows).
periodNoAggregation period for the report.day
segmentNoOptional Matomo segment filter, e.g. 'deviceType==mobile;country==DE'. See https://matomo.org/docs/segmentation/ for the syntax.
site_idYesNumeric Matomo site ID. If unknown, call matomo_list_sites first.
dimensionNoWhich device/technology dimension to report on.device_type
Behavior3/5

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

Annotations already indicate readOnlyHint=true, so the agent knows it's a safe read operation. The description adds the set of available dimensions (e.g., devices, brands, browsers), which is useful but does not go further to explain output format, pagination, or any side effects. This meets the baseline given the 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 a single, compact sentence that front-loads the purpose and lists key dimensions. There is zero redundant text, making it highly efficient and easy to parse.

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 reporting tool with a well-described schema and no output schema, the description is adequately complete. It covers the core dimensions and works with the schema's parameter details. It lacks an explicit note about output aggregations, but given the tool's simplicity and that all parameters are documented, this is sufficient.

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% with detailed descriptions for every parameter, so the baseline is 3. The description reinforces the dimension choices (matching the enum) but doesn't add substantive new meaning beyond what the schema already provides.

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 clearly states the tool reports on visitor devices and technology, enumerating specific dimensions such as device types, brands, browsers, and OS. It distinguishes itself by focusing on device/tech metrics, though it doesn't explicitly contrast with sibling tools like matomo_visits_summary.

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?

No guidance is provided on when to use this tool versus alternatives. The description simply lists what it reports; there is no mention of scenarios, exclusions, or recommendations such as 'use for device breakdowns' or 'prefer this for technology analysis'.

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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