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twelvebit Analytics

by app.twelvebit
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Visitors and revenue over time

get_timeseries
Read-onlyIdempotent

Visitors, page views and revenue per hour, day, week or month for a time range. Use it for trends, peaks and 'when did it change?'. The last bucket can be partial.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "get_timeseries"
  }
}

Input Schema

NameRequiredDescriptionDefault
toNoLast day, inclusive (YYYY-MM-DD).
fromNoFirst day (YYYY-MM-DD, site time zone). Use with to instead of period.
siteNoSite domain (example.com), id or public id (tb_…). Optional with a site key, or when the account has one site.
periodNoTime range. Default 30d. today, yesterday, 24h, 7d, 30d, 90d, 12m, wtd, mtd, ytd, all.
filtersNoOnly visits that match every filter. A value is "is": {"country": "US"}; a list is "is any of": {"country": ["PT", "ES"]}; an object picks the operator: {"page": {"contains": "/blog"}}, {"channel": {"is_not": ["Direct", "Email"]}}, {"page": {"not_contains": "/admin"}}. Operators: is, is_not, contains, not_contains (case-insensitive). "visitor" is "new" or "returning". "ad" is an ad campaign as "google:<campaign id>" or "meta:<campaign id>" (the visits that the Ads page counts for it). Use "(none)" for empty values; countries take ISO codes or English names.
granularityNoBucket size. Default depends on the range length.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4/5.0
Behavior4/5

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

Annotations already declare the tool read-only, idempotent and non-destructive, lowering the burden. The description adds a genuinely useful behavioral caveat—'The last bucket can be partial'—which is not derivable from annotations or schema. It stops short of explaining authentication, rate limits or other operational traits, but that is reasonable for this surface.

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?

Three short sentences, front-loaded with the core purpose and followed by usage cues and one important behavioral caveat. Every sentence carries information and there is no filler.

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?

Given a rich schema, complete annotations and no output schema, the description covers what the tool produces and a key edge case (partial final bucket). It could go slightly further in describing the response shape (e.g., per-bucket rows), but an agent has enough to invoke it correctly.

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%, so parameter meaning is almost entirely carried by the schema. The description's phrase 'per hour, day, week or month' loosely reflects the granularity enum, and 'The last bucket can be partial' adds a small amount of contextual meaning, but it adds no new detail about filters, ranges or the nested filter object.

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 states a specific resource (visitors, page views, revenue) and dimension (time buckets, granularity, range). It is clear what the tool returns, but it does not explicitly differentiate itself from siblings like get_breakdown, get_overview or get_revenue, so sibling differentiation is implicit at best.

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?

"Use it for trends, peaks and 'when did it change?'" gives clear positive guidance about when to reach for this tool. No exclusions or named alternatives are provided, so the agent must infer when not to use it.

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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