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Classify Segment Group

classify_segment_group

Classification costs credits (a flat per-person rate). The return carries the count of people the run will process and the credits it will charge (exact — the same population the run bills), so you can tell the user the cost; a run never charges more than the balance. If the balance can't cover the estimate the run is not enqueued — the return says so, and you should relay it. Dict with the in-scope person count and estimated credit cost, plus whether the run was enqueued or blocked for insufficient credits. Enqueued -> {"enqueued": true, "group_id", "person_count", "estimated_credits"}. Blocked -> {"enqueued": false, "reason": "insufficient_credits", "group_id", "person_count", "estimated_credits", "balance"}.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
forceNoRe-classify people already tagged for this group.
group_idYesThe segment group to classify (from list_segment_groups).
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
    • removedInput schema / properties / senders
      Removed value: -{
-  "anyOf": [
-    {
-      "items": {
-        "type": "string"
-      },
-      "type": "array"
-    },
-    {
-      "type": "null"
-    }
-  ],
-  "default": null,
-  "description": "In a shared workspace, the teammate email addresses whose people to tag — people\nwith a prospect owned by one of them. Omit to tag everyone in scope (including people\nno teammate has a prospect for). Addresses outside the workspace match nobody."
-}
  3. Changed1 schema field changed
    • addedInput schema / properties / senders
      Added value: +{
+  "anyOf": [
+    {
+      "items": {
+        "type": "string"
+      },
+      "type": "array"
+    },
+    {
+      "type": "null"
+    }
+  ],
+  "default": null,
+  "description": "In a shared workspace, the teammate email addresses whose people to tag — people\nwith a prospect owned by one of them. Omit to tag everyone in scope (including people\nno teammate has a prospect for). Addresses outside the workspace match nobody."
+}
  4. Added

TDQS

A4.8/5.0
Behavior5/5

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

Well beyond the annotations (readOnlyHint=false, destructiveHint=false): it discloses asynchronous execution, the ~15-minute auto-tagging window, credit cost at a flat per-person rate, the balance check that can block enqueueing, and the exact meaning of force. This is exactly the behavioral context annotations cannot carry.

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-loads the action and scope before cost and return details, and the returns block is detailed, partly duplicated by the returns section of the description. It is long but nearly every sentence carries decision-relevant information; the enqueued/blocked shapes are slightly redundant with the returns block.

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 compensates fully by specifying both return shapes (enqueued and insufficient_credits-blocked) and the side-effect timeline, so an agent can call it, report cost to the user, and relay a failure correctly.

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 baseline is 3, but the description adds real meaning the schema lacks: what force actually changes (re-classify everyone vs only never-classified people) and the cost implications of the run. group_id and on_behalf_of are already 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+resource+scope: kick off background classification of a group's in-scope canonical people. It is plainly distinguishable from siblings like estimate_segment_classify_scope, correct_segment_classification, and get_segment_rates.

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?

Explicitly says when to call (right after creating a group to tag existing people; with force=True after editing the group or its tags) and when not (without force only unclassified people are processed). It also routes the agent to the follow-up tools get_segment_rates and query_segment_people for results.

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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