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Glama

ExpenseBot

by io.github.TotesMagotes

get_income_summary

Read-only

Get income totals, breakdowns, and analytics from the Income tab. Covers Schedule C / T2125 income, Schedule B drill-in (interest, dividends, tax refunds, security deposits), rental income, and per-source / per-payment-method / per-category / per-month / per-tag breakdowns. Schedule-C-style category exclusions match year-end T6 routing (security deposits, refunds excluded from taxable totals). Examples: 'income YTD', 'income by source', 'rental income by property', 'interest income this year', 'dividends YTD', 'tax refunds 2024', 'income this year vs last' (YoY). Supports period comparison phrasing — YoY ('vs last year'), MoM ('vs last month'), QoQ ('Q1 vs Q2'), same-month-prev-year. Returns: { message, data: { total, breakdown?, comparison?, sampleMeta? } }.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryNoNatural language income question (e.g., 'income YTD by source', 'rental income last month')
groupByNoHow to group the breakdown
dateRangeNoTime period filter. Use exactly one variant — pick the shape that matches the user's phrasing.
incomeTagPrefixNoOptional 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.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Changed1 schema field changed
    • 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
  2. First observed

TDQS

A4.4/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true and destructiveHint=false, so the core safety profile is covered. The description adds valuable behavioral nuances beyond annotations: Schedule-C-style exclusions (security deposits, refunds excluded from taxable totals) and support for YoY/MoM/QoQ comparisons. It also reveals the exact return envelope ({ message, data: { total, breakdown?, comparison?, sampleMeta? } }), which is especially useful given no output schema.

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 longer than average but every component earns its place: the opening states scope, the middle provides precise tax/drill-down details, and the examples indicate common usage. It is front-loaded in effective order and avoids fluff. It could be tightened slightly, but it remains efficient for the tool's complexity.

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?

Given the tool's optional parameters, rich natural-language input, and absence of an output schema, the description is remarkably complete. It covers allowed date range shapes (month, quarter, year, specific, relative) through the schema, and the description adds overarching behavior like exclusions, comparison support, and the return envelope. An agent armed with this description and schema can invoke and interpret this tool with confidence.

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 description coverage is 100%, so baseline is 3, but the description adds real value. It clarifies how the 'query' parameter behaves, mapping natural language examples to groupBy values ('per-source', 'per-payment-method', 'per-category', 'per-month', 'per-tag') and explicating comparison semantics. This guidance is not present in the schema descriptions and helps agents use the tool correctly.

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?

Purpose is crystal clear: 'Get income totals, breakdowns, and analytics from the Income tab.' The description goes on to enumerate a precise set of income types (Schedule C/T2125, Schedule B drill-ins, rents) and breakdown dimensions (source, category, month, etc.), making it unmistakably distinct from income-related siblings like get_spending_summary. The scope exceeds a simple 'verb+resource' and fully specifies the domain.

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 provides extensive example user phrasings ('income YTD', 'rental income by property', 'income this year vs last'), which clearly map to when an agent should invoke this tool. However, it does not explicitly contrast it with alternatives or state when not to use it, leaving a slight gap in exclusions.

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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TDQS

A3.6/5.0
Disambiguation3/5

Most tools are explicitly scoped, but several analytics/retrieval tools overlap in purpose, such as get_spending_summary vs get_deep_analytics vs get_monthly_books_review, and generic search vs search_expenses vs search_knowledge. The detailed descriptions help, but an agent still has to carefully choose between near-equivalent options like correct_expenses vs update_expense and the three add_income variants.

Naming Consistency5/5

Tool names consistently use lower_snake_case with a recognizable verb prefix: get_*, list_*, add_*, create_*, check_*, scan_*, search_*, and whatif_*. Minor exceptions like fetch and search are still terse retrieval verbs rather than a different naming style, so the overall pattern is predictable.

Tool Count1/5

With 59 tools, this exceeds the 50+ threshold for an extreme tool count and creates a heavy selection surface for an agent. Even though ExpenseBot covers many subdomains, many get_/list_/add_ variants could be consolidated into fewer parameterized tools. The count undermines the otherwise clear naming structure.

Completeness3/5

The surface is strong for creating, reading, and updating expenses, reports, invoices, and Gmail scans, but there are notable lifecycle gaps: no delete/void tools for expenses, income, reports, or invoices, and no update tool for income. Several descriptions explicitly redirect unsupported edits to the web app, confirming that the assistant cannot complete those workflows directly.

Resources