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expense-mcp

expense-mcp

by justfsl50
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

expense_insights

Read-onlyIdempotent

Analyze monthly spending to reveal top categories, daily averages, peak spending days, and anomalies.

Instructions

Get spending patterns and insights for the current month.

Returns: Key spending metrics: top category, daily average, biggest day, anomalies.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "expense_insights"
  }
}

Input Schema

NameRequiredDescriptionDefault

No arguments

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.1

TDQS

A4.2/5.0
Behavior4/5

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

Beyond annotations (readOnly, non-destructive, idempotent), the description specifies that results are for the current month and includes return metrics like anomalies. This adds useful behavioral context, though it could detail anomaly detection criteria.

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 (two sentences) and front-loaded with the main purpose, followed by a clear list of return values. No superfluous content.

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

Completeness4/5

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

Given there are no parameters and an output schema exists, the description sufficiently explains the return metrics. However, it lacks explanation of what constitutes an anomaly, which would be beneficial for complete understanding.

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?

The tool has no parameters, and the input schema provides complete coverage. The description does not need to add parameter information, so it meets the baseline expectation for parameter semantics.

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 states the tool's purpose: retrieving spending patterns and insights for the current month. It distinguishes itself from sibling tools like expense_summary by focusing on insights such as top category, daily average, biggest day, and anomalies.

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

Usage Guidelines3/5

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

The description implies usage for obtaining monthly spending insights but does not explicitly guide when to use this tool over alternatives like expense_summary. No when-not-to-use or exclusion criteria are provided.

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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