Check whether ExpenseBot supports a specific feature ('does ExpenseBot support X', 'can it integrate with Y'). Searches the public knowledge base and returns a confidence-scored answer + related questions. Works with or without authentication.
| Name | Required | Description | Default |
|---|---|---|---|
| feature | Yes | Feature or integration to check (e.g., 'Xero', 'mileage tracking', 'Plaid') |
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?
Beyond the readOnlyHint and destructiveHint annotations, the description adds useful behavior: it searches the public knowledge base, returns a confidence-scored answer plus related questions, and works with or without authentication. There is no contradiction with the annotations.
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 concise and well-structured: first states purpose, then the search behavior and return shape, then authentication requirements. Every sentence contributes useful, non-redundant information.
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?
For a low-complexity tool with one required parameter and no output schema, the description is nearly complete: it explains what it checks, where it searches, what it returns, and auth requirements. It could add slightly more detail about the confidence-score format, but nothing essential is missing for correct invocation.
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 single 'feature' parameter is already fully described in the schema with concrete examples, so the description adds little semantic value beyond the schema. This meets the baseline for high schema_description_coverage.
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 states a specific action and resource: checking whether ExpenseBot supports a feature or integration, with quoted example questions. It is distinct from most siblings, though it does not explicitly contrast itself with similar knowledge-lookup tools like search_knowledge or search.
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 provides clear invocation context through canonical question forms ('does ExpenseBot support X', 'can it integrate with Y'), so an agent knows when this tool fits. It does not explicitly state when not to use it or name sibling alternatives.
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.