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Get Segment Rates

get_segment_rates
Read-only

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
channelNo'linkedin' or 'email' to bound the population; omit for both.
measureNo'accepted', 'replied', 'interested', or 'meeting'.replied
sendersNoIn a shared workspace, the teammate emails whose prospects to score (a group's `senders`). Omit to score every teammate. Any email not in the workspace is dropped; if that leaves no valid teammate, the result is empty — it does NOT fall back to the whole workspace.
group_idYesThe segment group to score (from list_segment_groups).
agent_idsNoScope to one or more campaigns (agent_tasks ids from a group's `campaigns`); omit for all campaigns.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed3 schema fields changed
    • addedInput schema / properties / agent_ids
      Added value: +{
+  "anyOf": [
+    {
+      "items": {
+        "type": "integer"
+      },
+      "type": "array"
+    },
+    {
+      "type": "null"
+    }
+  ],
+  "default": null,
+  "description": "Scope to one or more campaigns (agent_tasks ids from a group's `campaigns`); omit\nfor all campaigns."
+}
    • addedInput schema / properties / senders
      Added value: +{
+  "anyOf": [
+    {
+      "items": {
+        "type": "string"
+      },
+      "type": "array"
+    },
+    {
+      "type": "null"
+    }
+  ],
+  "default": null,
+  "description": "In a shared workspace, the teammate emails whose prospects to score (a group's\n`senders`). Omit to score every teammate. Any email not in the workspace is dropped; if\nthat leaves no valid teammate, the result is empty — it does NOT fall back to the whole\nworkspace."
+}
    • removedInput schema / properties / task_ids
      Removed value: -{
-  "anyOf": [
-    {
-      "items": {
-        "type": "integer"
-      },
-      "type": "array"
-    },
-    {
-      "type": "null"
-    }
-  ],
-  "default": null,
-  "description": "Scope to one or more campaigns (agent_tasks ids from a group's `campaigns`); omit\nfor all campaigns."
-}
  2. Added

TDQS

A4.1/5.0
Behavior4/5

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

Annotations declare readOnlyHint=true, and the description adds meaningful behavioral context beyond that: it explains the scoring unit (one data point per prospect-channel), how multi-team-member touches are handled (one data point per prospect), the synthetic 'No match' row behavior, and the confidence thresholds for `signal`. This is rich behavioral disclosure that helps an agent predict side effects and output shape.

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 description is well-structured with a summary and returns section, and it front-loads the core purpose. It is slightly dense but every sentence adds value—scoring semantics, edge cases, and related tools are all covered without fluff.

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?

For a read-only analytics tool with no output schema, the description covers the return structure, edge cases (untagged people, invalid senders), and confidence interpretation. It doesn't explicitly describe pagination or error behavior, but the provided context is sufficient for an agent to call it correctly and interpret results.

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 the schema already documents all five parameters. The description adds some context (e.g., `agent_ids` maps to a group's `campaigns` list, `senders` scopes to teammate emails), but most parameter meaning is already in the schema. Baseline 3 is appropriate.

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 description states a specific verb ('get') and resource ('per-tag outreach outcome rate for one measure over a segment group's classified people'), and gives concrete examples ('which seniority replies most', 'which role function books the most meetings'). It clearly distinguishes itself from sibling tools like get_message_tag_rates and query_segment_people by focusing on segment-group-level aggregate rates.

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?

The description explains when to use it (to see per-tag outcome rates for a segment group) and provides routing hints: pass `agent_ids` to scope to campaigns, and use `query_segment_people(untagged=true)` to list people classified as nothing. It doesn't explicitly name alternatives or exclusions, but the context is clear enough for an agent to select it over siblings.

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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