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Infinite Audience

by io.github.no-fait
OverviewDiscussionsAdmin

Get Audience Demographics

get_audience_demographics

Profile a saved audience composition, including its segment set logic and exclusions. Returns a BigQuery-computed demographic breakdown, including median age, gender split, homeowner %, children %, college-educated %, and high-income %. Results are cached server-side for 20 minutes. Requires 'discovery' scope.

Input Schema

NameRequiredDescriptionDefault
audience_idYesThe audience ID to retrieve demographics for

Output Schema

NameRequiredDescriptionDefault
guidanceNoInterpretation or recovery guidance for this result
count_unitNo
page_countNoNumber of objects on this page, not the organization total
audience_idYes
count_stateNoUnknown means no count; available does not guarantee freshness. Recount for a current value.
extra_fieldNo
total_countYes
generated_atYesISO timestamp of the demographic snapshot
next_actionsNoSuggested next calls using returned IDs and available scopes. Not executed and not user consent.
age_breakdownYes
audience_nameYes
geo_breakdownYes
money_displayNoCurrency amounts formatted once from source paths ending in _cents. Use verbatim; do not divide again. Null/missing source amounts are omitted, not zero.
breakdown_extraNo
population_inputNoPopulation input used for the preview; IDs refer to their current saved definitions

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • changedOutput schema / (root)
      Previous value: -nullNew value: +{
+  "$schema": "http://json-schema.org/draft-07/schema#",
+  "additionalProperties": {},
+  "properties": {
+    "age_breakdown": {
+      "items": {
+        "additionalProperties": {},
+        "properties": {
+          "label": {
+            "type": "string"
+          }
+        },
+        "required": [
+          "label"
+        ],
+        "type": "object"
+      },
+      "type": "array"
+    },
+    "audience_id": {
+      "type": "string"
+    },
+    "audience_name": {
+      "type": "string"
+    },
+    "breakdown_extra": {
+      "items": {
+        "additionalProperties": {},
+        "properties": {
+          "label": {
+            "type": "string"
+          }
+        },
+        "required": [
+          "label"
+        ],
+        "type": "object"
+      },
+      "type": "array"
+    },
+    "count_state": {
+      "description": "Unknown means no count; available does not guarantee freshness. Recount for a current value.",
+      "enum": [
+        "unknown",
+        "available"
+      ],
+      "type": "string"
+    },
+    "count_unit": {
+      "const": "records",
+      "type": "string"
+    },
+    "extra_field": {
+      "type": "string"
+    },
+    "generated_at": {
+      "description": "ISO timestamp of the demographic snapshot",
+      "type": "string"
+    },
+    "geo_breakdown": {
+      "items": {
+        "additionalProperties": {},
+        "properties": {
+          "label": {
+            "type": "string"
+          }
+        },
+        "required": [
+          "label"
+        ],
+        "type": "object"
+      },
+      "type": "array"
+    },
+    "guidance": {
+      "description": "Interpretation or recovery guidance for this result",
+      "type": "string"
+    },
+    "money_display": {
+      "additionalProperties": {
+        "type": "string"
+      },
+      "description": "Currency amounts formatted once from source paths ending in _cents. Use verbatim; do not divide again. Null/missing source amounts are omitted, not zero.",
+      "propertyNames": {
+        "type": "string"
+      },
+      "type": "object"
+    },
+    "next_actions": {
+      "description": "Suggested next calls using returned IDs and available scopes. Not executed and not user consent.",
+      "items": {
+        "additionalProperties": false,
+        "properties": {
+          "arguments": {
+            "additionalProperties": {},
+            "propertyNames": {
+              "type": "string"
+            },
+            "type": "object"
+          },
+          "reason": {
+            "type": "string"
+          },
+          "tool": {
+            "type": "string"
+          }
+        },
+        "required": [
+          "tool",
+          "arguments",
+          "reason"
+        ],
+        "type": "object"
+      },
+      "type": "array"
+    },
+    "page_count": {
+      "description": "Number of objects on this page, not the organization total",
+      "maximum": 9007199254740991,
+      "minimum": -9007199254740991,
+      "type": "integer"
+    },
+    "population_input": {
+      "additionalProperties": {},
+      "description": "Population input used for the preview; IDs refer to their current saved definitions",
+      "propertyNames": {
+        "type": "string"
+      },
+      "type": "object"
+    },
+    "total_count": {
+      "type": "number"
+    }
+  },
+  "required": [
+    "total_count",
+    "age_breakdown",
+    "geo_breakdown",
+    "generated_at",
+    "audience_id",
+    "audience_name"
+  ],
+  "type": "object"
+}
  2. First observed

TDQS

A3.8/5.0
Behavior4/5

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

Goes beyond the annotations by disclosing the auth requirement ('discovery' scope), the backend that computes the data (BigQuery), and a 20-minute server-side cache – all behaviorally relevant. The caching note is also useful given the annotations declare idempotentHint=false and readOnlyHint=false for what reads as a query operation, though the description never explicitly asserts a write, so this is not a direct contradiction.

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?

Three sentences, each earning its place: what it does, what it returns, then operational constraints (cache, scope). Purpose is front-loaded and nothing is redundant.

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?

With an output schema present, return-value detail is not the description's job, and it covers the important gaps (scope, caching, computation source). The only missing piece is routing guidance versus the segment-demographics sibling, which matters for an agent choosing among many demographic/count tools.

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% and the single audience_id parameter is fully documented there, so baseline 3 applies. The description adds no syntax, format, or case-sensitivity hints for the ID beyond what the schema provides.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb+resource ('Profile a saved audience composition') and enumerates the returned metrics, so it is clearly distinguished from a generic fetch. It does not explicitly name the segment-oriented sibling get_segment_demographics as the alternative, so differentiation is implicit rather than stated.

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

Usage Guidelines3/5

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

The description supplies a prerequisite ('Requires discovery scope') but gives no explicit when-to-use guidance and does not point to alternatives such as get_segment_demographics or count_segment. Usage must be inferred from the resource name.

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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