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simran-mehta

Expense Tracker MCP Server

by simran-mehta

get_expense_summary

Generate spending summaries grouped by category or month to analyze expense patterns and track financial habits.

Instructions

Summarize total spending grouped by 'category' or 'month'.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
group_byNocategory
start_dateNo
end_dateNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior2/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It states the aggregation function but doesn't mention whether this is a read-only operation (implied but not explicit), what permissions are required, how date ranges work (inclusive/exclusive), whether null dates mean 'all time', or what the output format looks like. For a summary tool with zero annotation coverage, this leaves significant behavioral gaps.

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?

The description is extremely concise - a single sentence that efficiently communicates the core functionality. Every word earns its place: 'Summarize' (action), 'total spending' (resource), 'grouped by' (operation), and the two grouping options. There's no wasted verbiage or redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's moderate complexity (aggregation with date filtering), no annotations, and the presence of an output schema (which handles return values), the description is minimally adequate. It covers the primary function but lacks important context about date parameter usage, behavioral constraints, and differentiation from sibling tools. The output schema existence prevents this from being a complete failure.

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?

The description mentions the 'group_by' parameter options ('category' or 'month'), which adds meaning beyond the schema's 0% description coverage. However, it doesn't explain the date parameters (start_date, end_date) at all - their purpose, format, or what happens when null. With 3 parameters total and only partial coverage in the description, this meets the baseline for moderate schema coverage compensation.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose with a specific verb ('Summarize') and resource ('total spending'), and specifies the grouping options ('by category or month'). It distinguishes this from siblings like list_expenses (which likely lists individual items) and get_budget_status (which focuses on budget metrics). However, it doesn't explicitly mention expense data as the resource, though this is implied.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention when to choose get_expense_summary over list_expenses (for aggregated vs detailed views) or get_budget_status (for spending vs budget comparison). There's no context about prerequisites, data scope, or typical use cases.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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