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Privatrak

by com.privatrak
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Funnel conversion over time

get_funnel_trend
Read-onlyIdempotent

One funnel's overall conversion and per-step visitor counts per day, week, month or year across a date range -- to tell whether a drop-off changed and when. Buckets with few entering visitors swing wildly; say so rather than reading a trend into them. Buckets marked partial cover only part of their week/month/year (see covers_from/covers_to) or contain today: never read a partial bucket as a drop. Dates are the project's calendar days, YYYY-MM-DD (timezone: get_project); timestamps are UTC. Omit from/to for the last 28 complete days, ending yesterday. A period marked starts_before_data begins before the project has data (see data_from): those days read as zero, not as a quiet period.

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": {
      "funnel_id": "<funnel_id>"
    },
    "name": "get_funnel_trend"
  }
}

Input Schema

NameRequiredDescriptionDefault
toNoLast day, YYYY-MM-DD in the project's timezone, inclusive. Default: yesterday, the last complete day.
fromNoFirst day, YYYY-MM-DD in the project's timezone, inclusive. Default: 27 days before 'to'.
traitNoOnly count visitors carrying this session trait value, the way the funnel's trait_scope says (see get_funnel).
projectNoProject ID, from list_projects. Required when this connection covers more than one project; may be left out when it covers one (and with an API key, which belongs to one project).
funnel_idYesFunnel ID, from list_funnels.
granularityNoBucket size: day (default), week, month or year.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.6/5.0
Behavior5/5

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

Annotations already establish read-only/idempotent/local behavior, but the description adds substantial behavioral context beyond them: low-sample buckets swinging wildly, partial buckets (with covers_from/covers_to or containing today) that must not be read as drops, and starts_before_data days reading as zero. It also clarifies timezone semantics and defaults.

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?

Front-loaded with the core purpose, then each subsequent sentence covers a distinct gotcha (sample size, partial buckets, timezone, defaults, data boundaries) so nothing is wasted. However, it is a dense single paragraph whose multiple caveats could be structured more scannably.

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

Completeness5/5

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

With no output schema, the description still conveys the shape and interpretation of the return data (per-step counts, bucket completeness, data-boundary zeros), which is what an agent needs to avoid misreading trends. Nothing essential to correct invocation or interpretation is missing.

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

Parameters4/5

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

Schema coverage is 100%, so the baseline is 3; the description adds meaning beyond the schema by explaining that dates are project calendar days while timestamps are UTC, and that omitting from/to yields the last 28 complete days ending yesterday. It doesn't elaborate the trait or granularity enums, but those are documented in the schema.

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?

States a specific verb and resource ('One funnel's overall conversion and per-step visitor counts') plus the bucketing dimension (per day/week/month/year) over a date range. This clearly distinguishes it from snapshot-style siblings like get_funnel and preview_funnel.

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?

Gives a clear motivating use case ('to tell whether a drop-off changed and when') that implies when this tool is appropriate versus a snapshot. It stops short of naming alternatives such as get_funnel explicitly, so it falls just short of the top band.

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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