ExpenseBot

get_monthly_books_review

Read-only

Month-end summary of the user's books for one calendar month: income recorded, money spent, net, top spending categories and merchants, plus alerts for anything unusual that month. Examples: 'how did last month go', 'close out my books for June', 'monthly review', 'what did I make and spend in May'. Defaults to the last completed month. Figures come from the user's own recorded data; advisory notes are estimates, not tax advice.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`period`	No	Calendar month in YYYY-MM format. Defaults to the last completed month.
`clientEmail`	No	Client account email. Accountants may use this only for an accepted ExpenseBot client.

Output Schema

TableJSON Schema

Name	Required	Description	Default
`data`	No
`message`	No
`success`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

Changed1 schema field changedSep 9, 2026

changedOutput schema / (root)

Previous value: -nullNew value: +{
+  "additionalProperties": true,
+  "properties": {
+    "data": {
+      "additionalProperties": true,
+      "type": "object"
+    },
+    "message": {
+      "type": "string"
+    },
+    "success": {
+      "type": "boolean"
+    }
+  },
+  "required": [
+    "success"
+  ],
+  "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

A4.1/5.0

Behavior4/5

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

Annotations already declare readOnlyHint=true and destructiveHint=false, covering the safety profile. The description adds valuable context: figures come from the user's own data, advisory notes are estimates and not tax advice, and it defaults to the last completed month. These disclosures go beyond the annotations and are relevant for an agent deciding whether to invoke this tool.

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 concise, front-loaded with the core summary, and includes examples and a caveat in a compact form. It avoids redundancy and stays focused. While it could be slightly more structured, it is efficient and easy to scan.

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 and annotations covering safety, the description adequately covers what the tool does, its default behavior, and a caveat about data sources. It does not mention edge cases like missing data for the month, but that is not critical given the output schema and overall completeness. The description provides sufficient context for an agent to call it correctly.

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 coverage is 100%, so both parameters (period and clientEmail) are fully documented in the input schema. The description reiterates the default for period but adds no new semantic detail beyond what the schema provides. Per the rubric, the baseline of 3 is appropriate when the schema already handles parameter documentation.

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 and resource: provides a month-end summary of the user's books for a calendar month, enumerating contents (income, spending, net, categories, merchants, alerts). It distinguishes itself from sibling tools like get_pnl and get_income_summary by focusing on a comprehensive monthly overview. The inclusion of example queries reinforces the tool's purpose.

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?

Clear context is provided via example queries ('how did last month go', 'close out my books for June'), indicating when to use this tool for month-end reviews. However, it does not explicitly name alternative tools or state when not to use it, so it lacks explicit exclusions. The context is strong enough for an agent to infer appropriate usage.

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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