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SaaS Pro Max

Get breakdown

get_breakdown
Read-onlyIdempotent

Top values for one analytics dimension, ranked by visitors: pages, entry and exit pages, referrer domains, sources, mediums, UTM parameters, countries, regions, cities, devices, browsers, operating systems, or event names.

Input Schema

NameRequiredDescriptionDefault
appYesApplication id, or orgSlug/appSlug as returned by list_apps
limitNo
rangeNoTime range: 24h, 7d, 30d, 90d, 12m, or custom:YYYY-MM-DD..YYYY-MM-DD. Defaults to 7d.
searchNo
filtersNoNarrow the read to exact values, as "dimension:value" pairs, e.g. ["country:DE", "source:google"]. Dimensions are the ones get_breakdown accepts and the first colon ends the dimension. Filters are combined with AND, cover at most the last 90 days, and the answer carries capped: true when the range had to be shortened.
dimensionYespath, entry_path, exit_path, referrer_domain, source, medium, utm_source, utm_medium, utm_campaign, utm_term, utm_content, country, region, city, device, browser, os or event
environmentNoEnvironment slug: production, staging or development. Defaults to production.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

B3.4/5.0
Behavior3/5

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

Annotations already declare readOnly, idempotent, non-destructive, open-world, so the safety profile needs no restating. The description adds the ranking metric (visitors) and that results are per-dimension, but says nothing about result size, default limit, or how the top-N cutoff interacts with filters. The capped/filter-shortening behavior is documented in the schema, not the 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?

A single front-loaded sentence that leads with the core operation. The long dimension list is somewhat redundant with the dimension property's own enum text and is the only part that doesn't earn its space.

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

Completeness3/5

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

With 7 parameters, no output schema, and no default stated for limit, an agent lacks detail on what comes back (values plus visitor counts? share?) and how 'top' is bounded. The description is adequate for choosing the tool but thin for predicting its output.

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 71%, so most parameters are documented in-schema. The description's dimension enumeration duplicates the dimension property description rather than adding meaning, and the undocumented parameters (limit, search) get no explanatory help. Baseline 3 is appropriate.

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?

States a specific verb+resource: returns top values for one analytics dimension, ranked by visitors. The enumerated dimension list makes the scope concrete. It doesn't name which sibling to use instead (get_timeseries, get_analytics_overview), but the operation itself is unambiguous.

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

Usage Guidelines3/5

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

Usage is only implied by 'top values ... ranked by visitors' — an agent can infer this is the categorical breakdown tool. There is no explicit when-to-use/when-not, no mention of when to prefer get_timeseries, get_metric_series, or get_analytics_overview instead.

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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