ExpenseBot

add_income

Log an income entry manually (cash, check, Stripe payout, etc.). Writes to the Income tab of the user's expense spreadsheet. Useful for income that isn't auto-detected from Gmail or Plaid. Call list_income_categories first and use one of its fixed tax categories; an omitted category defaults to Service income and an unknown category is rejected.

Input Schema

TableJSON Schema

Name	Required	Description
`tag`	No	Tag for client/project attribution
`date`	No	Income date (YYYY-MM-DD). Defaults to today.
`fees`	No	Processor/transfer fees deducted
`notes`	No
`amount`	Yes	Income amount (>0)
`source`	Yes	Who paid you (client name, customer, etc.)
`category`	No	A category returned by list_income_categories (optional; defaults to Service income)
`currency`	No	Currency code (default: home currency)
`reference`	No	Invoice or transaction reference
`description`	No	What the income was for
`taxCollected`	No	Sales tax/GST/HST collected
`paymentMethod`	Yes	How you got paid. Canonical rails are Cash, Check, Bank transfer, Wallet app, Credit/Debit card, Payment processor, or Other; common labels such as Stripe and Venmo are normalized.

Schema Changelog

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

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?

Beyond the annotations, the description discloses a concrete write target (Income tab of the spreadsheet), the category validation behavior (unknown category rejected), and the default category. The non-destructive hints and title are not contradicted. It does not describe the return value or whether the entry is immediately visible, but the disclosed side effect is the main behavior an agent needs.

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 carry the action, side effect, use-case, prerequisite, default, and validation behavior with no filler. The most decision-relevant content is front-loaded.

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?

For a mutation tool with 12 parameters and no output schema, the description covers the workflow and critical validation behavior well enough for an agent to call it correctly. It could go a bit further by describing what the successful call returns or by explicitly differentiating itself from the file/CSV income siblings.

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 coverage is high at 92%, so the schema already explains most parameters; 3 is the baseline. The description adds genuinely valuable semantics beyond the schema: categories must come from list_income_categories, an omitted category defaults to Service income, and unknown categories are rejected. It also gives payment-method examples that map to accepted input values.

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: 'Log an income entry manually' and states it writes to the Income tab of the user's expense spreadsheet. It also distinguishes itself from the auto-detection siblings by framing this as the tool for income that is not detected from Gmail or Plaid.

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?

It clearly gives the when-to-use context: manual income that is not auto-detected from Gmail or Plaid. It also gives a strong prerequisite by requiring list_income_categories to be called first. It does not explicitly name the sibling add_income_from_csv/add_income_from_file as the batch/alternate path, which keeps it just short of fully explicit alternative routing.

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.

add_income

Input Schema

Schema Changelog

TDQS

Glama MCP Gateway

TDQS

Resources