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Statable Analytics

get_tracking_settings

Check which tracking features are enabled, which are available, and how many bytes each adds to your analytics script. View locked features and dependencies to decide what can be adjusted.

Instructions

What the installed script actually does: which tracking features are on, which exist, and what each costs in bytes. Response: {"site_id":123,"version":N,"bundle":"...","enabled":["hash","outbound"],"features":[{"id":"outbound","label":"...","enabled":true,"locked":false,"default":false,"requires":[...],"size_br":123}]}. Locked features cannot be changed; requires lists features that must be on for this one to work. Needs the same access as changing them (see update_tracking_settings).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
siteYessite_id from list_sites.

Schema Changelog

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

  1. First observedv0.1.0

TDQS

A4.3/5.0
Behavior5/5

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

With no annotations provided, the description carries the full behavioral burden and delivers: an inline JSON response example showing the exact return shape, semantic disclosure of locked (cannot be changed) and requires (must be on for the feature to work), and an explicit access requirement. This is far above the typical read-tool description.

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 purpose is front-loaded and every sentence earns its place: response shape, field semantics, and the access note. The inline JSON example is verbose but justified because there is no output schema to carry the return-format burden.

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 one-parameter read tool with no output schema and no annotations, the description is nearly complete: it documents the return format inline, explains non-obvious field semantics, and states access requirements. The only real gap is error behavior on an invalid site_id.

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 description coverage is 100% — the site parameter is already documented as 'site_id from list_sites.' The description adds no parameter-level detail beyond the schema, so the baseline 3 applies.

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 states exactly what the tool retrieves: which tracking features are on, which exist, and what each costs in bytes. It also differentiates itself from the sibling update_tracking_settings by framing the content as what the installed script actually does, so an agent can distinguish read from write without opening either schema.

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 routes the agent to update_tracking_settings for the change operation, implying this tool is for reading, and notes the access prerequisite ('needs the same access as changing them'). It does not explicitly enumerate when-not-to-use cases or alternative siblings like get_tracking_snippet, but the read/write contrast gives clear context.

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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