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Glama

ExpenseBot

by io.github.TotesMagotes

get_spending_summary

Read-only

Summarize the user's recorded expenses with totals and breakdowns by category, merchant, month, tag, source, or payment method. Supports date ranges, period comparisons, and total, count, or average metrics. Read-only. Returns: { message, data: { total, breakdown?, comparison?, sampleMeta? } }.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryNoNatural language question (e.g., 'how much did I spend in March', 'top merchants this quarter')
metricNo
groupByNo
dateRangeNoTime period filter. Use exactly one variant — pick the shape that matches the user's phrasing.
categoriesNoOptional configured expense categories to include in the summary.
clientEmailNoClient account email. Accountants may use this only for an accepted ExpenseBot client.

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. Changed1 schema field changed
    • addedInput schema / properties / categories / description
      Added value: +"Optional configured expense categories to include in the summary."
  3. First observed

TDQS

A4.2/5.0
Behavior4/5

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

Annotations already cover the read-only and non-destructive nature, so the description's 'Read-Only' line adds little there. However, it does disclose the return payload shape ('data: { total, breakdown?, comparison?, sampleMeta? }') and explicitly states that date ranges, period comparisons, and multiple metrics are supported, which is useful behavior beyond what annotations provide.

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 carrying distinct information: what the tool does, what filtering/aggregation it supports, and what it returns. There is no filler. It is front-loaded with the primary action and compactly conveys the return schema.

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 6 optional parameters, no output schema, and a natural-language `query` parameter, the description does most of what an agent needs: it states capabilities, breakdown axes, metrics, and the return shape. It does not explicitly explain how to divide responsibility between the `query` and `dateRange` parameters, but the schema already offers detailed guidance there, so this is a minor gap.

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 67%; the parameters that lack descriptions appear to be `metric` and `groupBy`. The description compensates by listing the supported grouping options (category, merchant, month, tag, source, payment method) and metrics (total, count, average). It also reinforces the dateRange behavior by mentioning date ranges and period comparisons.

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 opens with a specific verb and resource: 'Summarize the user's recorded expenses,' and then enumerates the exact grouping dimensions (category, merchant, month, tag, source, payment method). The 'spending' scope clearly distinguishes this from sibling tools like get_income_summary or search_expenses.

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 makes it clear the tool is for aggregate expense summaries, but it provides no explicit guidance about when to choose it over financial summary siblings such as get_income_summary, get_deep_analytics, or get_pnl. There is also no when-not-to-use guidance. The usage is implied rather than stated.

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