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Glama

ExpenseBot

by io.github.TotesMagotes

get_trip_suggestions

Read-only

Show ExpenseBot's current high-confidence Trip Intelligence proposals from Review Expenses. Read-only: never groups, tags, creates reports, or writes to the spreadsheet. Each proposal returns strict YYYY-MM-DD dates, evidence, locations, and the exact Receipt IDs with expected current tags. Do not infer a client/project from geography. When nextAction is present, use exactly its group_expenses params with confirm:false, show the canonical preview, then ask for explicit confirmation before any write. Never send confirm:true from this read result alone. In accountant clientEmail mode this tool can read accepted clients but may return no executable nextAction; hand off to Review Expenses or the owner's own assistant connection to apply.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMaximum high-confidence proposals to return (default 3).
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. Added

TDQS

A4.7/5.0
Behavior5/5

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

The description goes well beyond the readOnlyHint and destructiveHint annotations by explaining strict date formatting, tagged evidence, the 'never infer client/project from geography' rule, and the mandatory confirmation flow around nextAction. There is no contradiction between the text and annotations.

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?

Though the description is dense, every sentence earns its place: scoping, return contents, inference guards, confirmation flow, and accountant handoff. The most important 'read-only' signal is front-loaded, and no filler phrasing weakens the message.

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?

Even though there is no output schema, the description enumerates what each proposal returns: strict dates, evidence, locations, receipt IDs, and expected tags. It also covers the two important edge contexts (nextAction present and accountant clientEmail mode), so the agent has enough to call the tool correctly and know what to do next.

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 coverage is 100%, and the input schema already documents limit bounds and the clientEmail accepted-client restriction. The description adds useful contextual nuance around accountant-mode behavior, but it does not need to re-explain the parameters, and it does not add significant detail beyond the schema.

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: 'Show ExpenseBot's current high-confidence Trip Intelligence proposals.' It also states what the tool is not ('never groups, tags, creates reports, or writes to the spreadsheet'), which distinguishes it from nearby sibling tools. The scope is unmistakable even before reading the schema.

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?

The description gives explicit when-to-use behavior: read Trip Intelligence suggestions from Review Expenses, return proposals, and use nextAction only with confirm:false. It also gives a when-not-to-use rule in accountants mode: if nextAction is absent, hand off to Review Expenses or the owner's assistant rather than executing writes from this read result alone.

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