ExpenseBot

get_income_summary

Read-only

Use this when the user asks about income already recorded in their ExpenseBot Income tab. Read totals; group by source, category, month, payment method or tag; filter by dateRange/incomeTagPrefix. Query supports period comparisons (e.g. 'income YTD by source', 'income this year vs last'). Returns message and data with total and optional breakdown, comparison, sampleMeta. No scanning, imports, mutations, tax returns or liability determination.

Input Schema

TableJSON Schema

Name	Required	Description
`query`	No	Natural language income question (e.g., 'income YTD by source', 'rental income last month')
`groupBy`	No	How to group the breakdown
`dateRange`	No	Time period filter. Use exactly one variant — pick the shape that matches the user's phrasing.
`incomeTagPrefix`	No	Optional tag-prefix shortcut (e.g., 'Prop –' for rental income, 'Client –' for client billings, 'Wedding –' for events). When set, the tool filters to income rows tagged with this prefix.

Output Schema

TableJSON Schema

Name	Required	Description
`data`	No	Inline branch: computed analytics, or null when no spreadsheet / no data.
`type`	No	Async branch only: "analytics_pending" — poll pollEndpoint or call poll_analytics with jobId.
`jobId`	No	Async branch only.
`message`	No	Human-readable narrative (both branches).
`success`	No	Async branch only.
`response`	No	Chat alias of message.
`pollEndpoint`	No	Async branch only.
`pollInterval`	No	Async branch only: milliseconds.
`estimatedTime`	No	Async branch only: seconds.

Schema Changelog

Changes observed during successful MCP inspections.

Changed1 schema field changedSep 9, 2026

changedOutput schema / (root)

Previous value: -nullNew value: +{
+  "additionalProperties": true,
+  "description": "Two branches. Async: type='analytics_pending' with jobId — poll poll_analytics until complete. Inline: message + data with the computed analytics (data may be null when there is no spreadsheet or no data yet). success is present only on the async branch.",
+  "properties": {
+    "data": {
+      "additionalProperties": true,
+      "description": "Inline branch: computed analytics, or null when no spreadsheet / no data.",
+      "type": [
+        "object",
+        "null"
+      ]
+    },
+    "estimatedTime": {
+      "description": "Async branch only: seconds.",
+      "type": "number"
+    },
+    "jobId": {
+      "description": "Async branch only.",
+      "type": "string"
+    },
+    "message": {
+      "description": "Human-readable narrative (both branches).",
+      "type": "string"
+    },
+    "pollEndpoint": {
+      "description": "Async branch only.",
+      "type": "string"
+    },
+    "pollInterval": {
+      "description": "Async branch only: milliseconds.",
+      "type": "number"
+    },
+    "response": {
+      "description": "Chat alias of message.",
+      "type": "string"
+    },
+    "success": {
+      "description": "Async branch only.",
+      "type": "boolean"
+    },
+    "type": {
+      "description": "Async branch only: \"analytics_pending\" — poll pollEndpoint or call poll_analytics with jobId.",
+      "type": "string"
+    }
+  },
+  "required": [],
+  "type": "object"
+}

Changed1 schema field changedAug 25, 2026

changedOutput schema / (root)

Previous value: -{
-  "additionalProperties": true,
-  "description": "Standard ExpenseBot tool result envelope. `message` is the human-readable summary the AI cites; `data` is the structured payload (totals, breakdowns, ids, etc.). On failure, `success` is false and `error` carries a code/message/hint triple.",
-  "properties": {
-    "data": {
-      "additionalProperties": true,
-      "description": "Structured payload. Shape varies per tool — common keys: total, breakdown, comparison, sampleMeta, ids, expenseId, reportId, signupUrl, results.",
-      "type": "object"
-    },
-    "error": {
-      "additionalProperties": true,
-      "description": "Present only when success === false.",
-      "properties": {
-        "code": {
-          "type": "string"
-        },
-        "hint": {
-          "type": "string"
-        },
-        "message": {
-          "type": "string"
-        }
-      },
-      "type": "object"
-    },
-    "message": {
-      "description": "Human-readable result text. Always present on success; prefer rendering this verbatim before any further reasoning.",
-      "type": "string"
-    },
-    "sampleMeta": {
-      "additionalProperties": true,
-      "description": "Set when the underlying dataset was truncated. isTruncated=true means the agent saw a sample of `sampleCount` of `totalCount` rows; aggregate totals are still accurate.",
-      "properties": {
-        "isTruncated": {
-          "type": "boolean"
-        },
-        "sampleCount": {
-          "type": "integer"
-        },
-        "totalCount": {
-          "type": "integer"
-        }
-      },
-      "type": "object"
-    },
-    "success": {
-      "description": "False on tool errors; check before reading `data`.",
-      "type": "boolean"
-    }
-  },
-  "type": "object"
-}New value: +null

First observedAug 10, 2026

TDQS

A4.4/5.0

Behavior4/5

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

Annotations already declare readOnly/openWorld=false/destructive=false, and the description adds real context beyond them: it discloses the return shape (message plus data with total, optional breakdown, comparison, sampleMeta) and draws hard boundaries against scanning, importing, or mutating. It does not discuss pagination or result limits, so it falls short of a 5.

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-loaded with the trigger condition, then scope, then return shape, then exclusions — a sensible ordering with no filler sentences. It is fairly dense, packing five groupBy values and multiple exclusions into two sentences, but nothing is wasted.

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?

An output schema exists, so return values need not be explained further; the description covers trigger, scope, grouping/filtering dimensions, comparison support, and explicit non-goals. Nothing an agent needs to invoke this correctly is missing.

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 query, groupBy, dateRange variants, and incomeTagPrefix in detail. The description largely restates the groupBy enum values and filter fields rather than adding new meaning, so the baseline 3 applies.

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 (read totals) and resource (income recorded in the ExpenseBot Income tab) and enumerates the grouping and filtering dimensions. It is clearly distinguishable from siblings like get_pnl, get_spending_summary, and get_mileage_summary without opening any schema.

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?

Explicit trigger ('Use this when the user asks about income already recorded in their Income tab') plus an explicit exclusion list ('No scanning, imports, mutations, tax returns or liability determination') that routes those needs away from this tool. Both when-to-use and when-not are covered.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All ExpenseBot tools Inspect

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