Lunch Money MCP Server

getTransactions

Retrieve and filter financial transactions from Lunch Money by date range, category, tags, account, or status to analyze spending patterns.

Instructions

List transactions with advanced filtering options including date range, category, tags, account, and status

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": "getTransactions"
  }
}

Input Schema

Name	Required	Description	Default
`start_date`	No
`end_date`	No
`category_id`	No
`tag_id`	No
`account_id`	No
`debit_as_negative`	No
`pending`	No
`status`	No
`offset`	No
`limit`	No

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Jan 28, 2026

TDQS

C2.8/5.0

Behavior2/5

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

With no annotations provided, the description carries full burden for behavioral disclosure. It states it's a list operation with filtering, implying read-only behavior, but doesn't cover critical aspects like pagination (offset/limit usage), authentication needs, rate limits, or response format. For a 10-parameter tool with no annotation coverage, this leaves significant gaps in understanding how the tool behaves.

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 a single, efficient sentence that front-loads the core purpose ('List transactions') and immediately specifies key capabilities. Every word earns its place with no redundancy or wasted text.

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

Completeness2/5

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

Given the complexity (10 parameters, no annotations, no output schema), the description is incomplete. It covers the filtering purpose but misses behavioral context (pagination, auth, response), half the parameters, and output details. For a tool with rich input schema but zero structured documentation elsewhere, this leaves the agent under-informed.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must compensate. It lists filtering options (date range, category, tags, account, status) which covers 5 of the 10 parameters, but omits 'debit_as_negative', 'pending', 'offset', and 'limit'. The description adds some meaning for half the parameters but fails to address the other half, including pagination controls.

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 verb ('List') and resource ('transactions'), making the purpose evident. It specifies 'advanced filtering options' which distinguishes it from simpler list operations, though it doesn't explicitly differentiate from potential sibling list tools like 'getAssets' or 'getCategories' beyond the resource type.

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 mentions 'advanced filtering options' but provides no guidance on when to use this tool versus alternatives like 'getTransactions' without filtering or other sibling tools. It lacks explicit when/when-not instructions or named alternatives, leaving usage context implied at best.

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