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Glama

ExpenseBot

by io.github.TotesMagotes

get_pnl

Read-only

Compute Profit & Loss (P&L / net income / margin) by combining the Income tab with expense tabs. Examples: 'am I profitable this year', 'P&L for Q1', 'net income last quarter', 'what's my margin', 'P&L this year vs last' (YoY). Supports period comparison — YoY, MoM, QoQ, same-month-prev-year. Margin renders as multiplier in loss territory ('expenses 5.4× revenue') so the user gets a readable signal instead of '-436.9% margin'. Returns: { message, data: { revenue, expenses, netIncome, margin, comparison?, sampleMeta? } }.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryNoNatural language P&L question (e.g., 'P&L for Q1 2025', 'am I profitable')
dateRangeNoTime period filter. Use exactly one variant — pick the shape that matches the user's phrasing.
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. First observed

TDQS

A4.4/5.0
Behavior5/5

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

With readOnlyHint=true and destructiveHint=false in annotations, the description adds real behavioral value: it discloses that margin is rendered as a multiplier in loss territory ('expenses 5.4× revenue') instead of a confusing negative percentage, and it documents the exact return shape. This prevents the agent from misinterpreting an unusual output.

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?

The description is information-dense and efficiently ordered: core operation, representative queries, period-comparison capability, and even the unusual margin formatting every sentence earns its place. The compact return structure makes the black-box behavior predictable without over-explaining.

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?

Since there is no output schema, the description appropriately provides a return shape and even documents a surprising formatting behavior. It does not mention what happens when no dateRange is provided, however, which is relevant because all parameters are optional, and "sampleMeta?" remains unexplained.

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 baseline of 3 applies. The description's example date ranges and comparison support add context about how query and dateRange may be phrased, but the schema already carries the semantic weight for parameter shapes, especially the dateRange oneOf variants.

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 precise verb+resource statement — 'Compute Profit & Loss (P&L / net income / margin) by combining the Income tab with expense tabs' — so an agent understands exactly what the tool computes. Examples ranging from 'am I profitable this year' to YoY comparisons make the tool's scope concrete and distinguish it from income-only or spending-summary siblings.

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 clear use contexts through query examples and explicitly names period-comparison modes (YoY, MoM, QoQ, same-month-prev-year). It does not explicitly say when NOT to use it or name an alternative like get_per_tag_pnl or get_income_summary, so it falls just short of a 5.

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

All ExpenseBot toolsInspect
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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