Fetch a single expense row by its sheet row number or by its ExpenseBot expenseId (the exact Receipt ID in Column Q). Returns the row's headers + values + a labeled {header → value} map so you can refer to a specific expense the user mentioned. Read-only.
| Name | Required | Description | Default |
|---|---|---|---|
| expenseId | No | Exact full ExpenseBot Receipt ID from Column Q | |
| rowNumber | No | 1-indexed sheet row (row 1 is headers, so ≥ 2) |
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?
Annotations already declare readOnlyHint=true and destructiveHint=false, and the description reinforces this with 'Read-only.' Beyond that, it discloses the return format (headers + values + labeled map) which is not self-evident from the schema. This adds useful behavioral context, making the agent aware of what to expect from the response.
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?
The description is two sentences long, with the core action and identification methods front-loaded. Each clause serves a purpose: identifying the input, specifying the output, and noting the intended use case. There is no redundancy or filler, making it an efficient and well-structured definition.
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?
The tool is simple with optional parameters, read-only semantics, and no output schema. The description covers all essential aspects: how to identify the expense, what the return value contains, and the typical use case. Given the absence of an output schema, the explicit description of the returned map compensates fully. Nothing an agent needs to call this correctly appears missing.
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?
The schema provides full descriptions for both parameters (expenseId and rowNumber), including the exact source ('Column Q') and row indexing rules. The description adds no new semantic value beyond what the schema already states—it repeats the identification methods without further elaboration. Since schema coverage is 100%, the baseline of 3 is appropriate.
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?
The description clearly identifies the tool as fetching a single expense row by two possible identifiers (row number or ExpenseBot expenseId) and explicitly states the output shape. This distinguishes it from general search or list tools in the sibling set, which address different needs.
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?
The description states when to use it ('so you can refer to a specific expense the user mentioned'), providing a clear use-case. It does not explicitly contrast with alternatives like search_expenses or list-related tools, but the specificity of the operation ('fetch a single expense row') makes the intended context evident. The use-case guidance is sufficient, though a note on when not to use it would be stronger.
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.