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Correct Segment Classification

correct_segment_classification

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
tag_idsYesThe corrected tag ids (from the group's tags). At most one for a single-tag group; an empty list clears the person to "No match".
group_idYesThe segment group the person is classified under (from list_segment_groups).
person_idYesThe canonical person to correct (from query_segment_people / query_people).
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. Changed1 schema field changed
    • changedInput schema / properties / tag_ids / description
      Previous value: -"The corrected tag ids (from the group's tags). At most one for a single-tag group;\nan empty list clears the person to \"no tag\"."New value: +"The corrected tag ids (from the group's tags). At most one for a single-tag group;\nan empty list clears the person to \"No match\"."
  3. Added

TDQS

A4.5/5.0
Behavior5/5

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

Adds substantial behavior beyond annotations: it replaces the person's tags, empty tag_ids clears to "No match", group rates and people views recompute immediately, and the override persists so later re-tagging won't re-classify the person. This is exactly the mutation and persistence context an agent needs.

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?

Front-loads the action and scope, then covers key edge case behavior, immediate effects, and persistence. The separate returns block is concise and useful; there is no filler.

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?

Although no output schema exists, the return shape is described in a compact returns block. Required parameters, valid sources for ids, edge cases, immediate effects, and persistence are all covered well enough for correct invocation.

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 all four parameters are already documented. The description reinforces provenance for person_id and tag_ids and notes single-tag group behavior, but mostly restates what the schema provides.

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: correct the tags on one person when the classifier was wrong. It scopes the action to one (group, person) and explicitly calls itself the human-set override, which distinguishes it from group-level and message-level classification siblings.

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 clear context for use: when the classifier has mis-tagged one person, and gives the provenance for person_id and tag_ids. It does not explicitly name classify_segment_group as the bulk alternative, so it stops short of full when/when-not routing.

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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