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Glama

ExpenseBot

by io.github.TotesMagotes

check_tax_deductibility

Read-only

Look up whether a specific expense type or purchase is tax-DEDUCTIBLE (business write-off rules) based on the user's country/jurisdiction. This is a deductibility advice tool — use it for questions like 'is Uber deductible', 'can I write off home office', 'are client dinners 50% or 100%'. Do NOT use it for questions about tax REFUNDS received as income (use get_spending_summary with a 'tax refunds' query for those — they're routed to the Income tab, not deduction rules).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYesDeductibility question (e.g., 'is Uber tax deductible', 'home office write-off rules')
categoryNo
merchantNo

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

A4.3/5.0
Behavior4/5

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

Annotations already mark read-only/non-destructive, so the description only needs adding context beyond that. It adds 'advice' framing and country/jurisdiction dependence, and explicitly disclaims tax-refund handling. It does not disclose edge-case behavior about unresolved countries, but for a read-only advice tool this is a minor gap.

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?

Two sentences: the first states purpose with tight examples, the second draws the sibling-distinction and adds exact routing instruction. There is no filler and no repeated structural metadata.

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 tool with no output schema and only one required parameter, the description carries the main operational context well: what question it answers, when to avoid it, and which sibling to use instead. It doesn't state what exactly the tool returns or the place of optional filters, but these are less impactful given the advice-tool framing.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is only 33%, with 'category' and 'merchant' lacking descriptions. The tool description heavily reinforces 'query' with examples, but never explains whether/how 'category' and 'merchant' should be filled or how they combine with 'query'. This leaves a meaningful invocation ambiguity.

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 names a specific action ('look up'), a specific resource ('tax-deductibility ... based on country'), distinguishes brother(s" and grounds with concrete examples. It is clearly a look-role and not a gauge or a general expense tool.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

It provides explicit with-type examples and an explicit when-not-to-use: questions about received tax refunds are routed to get_spending_summary instead. This is a precise decision rule with a named sibling.

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