Process specific Gmail emails as receipts. Pass Gmail message IDs and they'll be converted to PDF, extracted by AI, and added to the user's expense spreadsheet. Max 25 emails per request. Requires Gmail to be connected in ExpenseBot settings.
| Name | Required | Description | Default |
|---|---|---|---|
| emailIds | Yes | Gmail message IDs to process as receipts | |
| accountEmail | No | Optional: which Gmail account to use (for users with multiple linked accounts) |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Output 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: +nullDoes the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description goes beyond annotations by explaining the side effects: emails are converted to PDF, extracted by AI, and added to the expense spreadsheet. It also sets expectations with the 25-email cap and prerequisite. It does not cover outcomes like duplicate handling or whether emails are modified.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Four short sentences that front-load the core purpose, then give the call pattern, limits, and prerequisite. No fluff or redundant restating of the tool name or schema.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Covers the core behavior, input scope, practical limit, and the key prerequisite, which is enough for an agent to understand how to call the tool. However, given there is no output schema, it would be stronger to mention how the agent can check the result, such as a return value or linking to a related status tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Input schema already covers both parameters with 100% description coverage, so the description does not need to repeat schema details. It adds the request limit of 25 emails, which is useful, but doesn't deeply explain parameter semantics beyond what the schema provides.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
States a specific operation: process specific Gmail emails as receipts, and describes the full pipeline (convert to PDF, extract with AI, add to expense spreadsheet). This clearly differentiates it from scanning siblings like scan_gmail or scan_gmail_years.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides clear context: pass Gmail message IDs, limited to 25 per request, and requires Gmail connection in ExpenseBot settings. It does not explicitly name alternatives like scan_gmail or state when not to use this tool, so it stops 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.
Add one secure layer between your agents and this server.
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.
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.
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.
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.