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Get Segment Funnel

get_segment_funnel
Read-only

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
agent_idYesThe campaign (agent) whose funnel to split.
group_idNoThe segment group to split by, from `groups`. Omit for the first group that tags this campaign.
on_behalf_ofNoAn agency client's email, from list_agency_clients, to run this call in that client's account: their agents, settings, connected accounts, and credits. Omit it to act on your own account.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • addedInput schema / properties / on_behalf_of
      Added value: +{
      +  "anyOf": [
      +    {
      +      "type": "string"
      +    },
      +    {
      +      "type": "null"
      +    }
      +  ],
      +  "default": null,
      +  "description": "An agency client's email, from list_agency_clients, to run this call in that client's account: their agents, settings, connected accounts, and credits. Omit it to act on your own account."
      +}
  2. Added

TDQS

A4.8/5.0
Behavior5/5

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

Beyond the readOnlyHint annotation, the description discloses non-obvious behavior: each person is counted once so rates diverge from get_segment_rates, `funnel.reached` is cumulative while other stage keys are current-stage-only, signal thresholds (thin/early/solid), the 'No match' tag, multi-tag double counting, and the ~15 minute tagging lag.

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 summary is front-loaded with the definition and the key caveat before the returns block, and every clause carries information. It is long, but the length is justified because there is no output schema to carry the return-shape documentation.

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 fully documents the return shape (groups, selected, funnel, signal, not_tagged, out_of_credits) and the semantics of each field, so an agent can both call and interpret the result without guessing.

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 all three parameters are already documented, but the description adds interpretive meaning by tying agent_id/group_id to the funnel's output structure and to the drill-down call in query_agent_people, and by explaining what omitting group_id yields (selected: null when nothing covers the campaign).

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 first sentence gives a specific verb+resource+scope: one campaign's lead funnel split by a segment group's tags, with concrete example questions. It explicitly distinguishes itself from the sibling get_segment_rates and explains the counting difference (one person once vs. per-channel).

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

Usage Guidelines5/5

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

It states when to use it (tag-filtered funnel questions like 'how are VPs doing'), names the alternative get_segment_rates and the condition that selects it, and routes the agent to query_agent_people with the exact arguments needed to drill into the people behind a number.

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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