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Glama

ExpenseBot

by io.github.TotesMagotes

request_accounting_integration

Request support for an accounting package only after checking ExpenseBot's reviewed direct and import-file destinations and finding no matching package. Do not use this for a listed or Beta destination, a general compatibility question, or without the exact package name. This is a confirmation-gated write: after the user approves, it creates one deduplicated request bound to the authenticated ExpenseBot account and emails ExpenseBot's internal team. It does not create an integration or make the requested package immediately available. On accepted or previously recorded requests, tell the user ExpenseBot will email them within one week with an update.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
packageNameYesExact accounting package requested by the user.

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

A4.8/5.0
Behavior5/5

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

The description fully discloses behavioral traits beyond the annotations. It states the user must approve before action (confirmation-gated), it creates a deduplicated request, it does not create an integration or make the package immediately available, and it emails ExpenseBot's internal team. It also explains what to tell the user on accepted or previously recorded requests (one-week email update). The annotations provide structural hints but the description's transparency is rich and decisive.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is dense but not verbose. It front-loads the critical condition (check destinations first) and then explains the action, the non-effect, and the follow-up. Every sentence is functional and no content is wasted. Minor suggestion: could be a touch more compact, but it's strong.

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?

Despite no output schema, the description is self-contained and fully describes the expected behavior, what to the user, and the temporal after-effect. The agent can correctly invoke it and also know what to tell the user. Given the one-parameter tool and multiple interaction steps, this is complete.

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 100%, so the annotation baseline is 3. The description adds meaningful constraint on the packageName: it must be the exact package name. The description also implies the parameter must be a tested-for package, and the 'exact' requirement is important. This rises slightly above baseline.

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 clearly identifies the tool's purpose: request support for an accounting package that is not already a reviewed destination. It specifies the exact action (creating a deduplicated request bound to the authenticated account and emailing the internal team) and distinguishes it from actually creating an integration.

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 provides explicit usage conditions: only use after checking ExpenseBot's reviewed direct and import-file destinations and finding no matching package. It also says when NOT to use it (listed/Beta destination, general compatibility question, without exact package name). This provides clear decision guidance.

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