ledger-mcp

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search_transactions

Filter and retrieve rent ledger transactions by date, category, entry type, account, source, text, or amount, returning matching entries and total count.

Instructions

Search ledger transactions with flexible filters.

Filters combine with AND. entry_types accepts charge/payment/credit/ refund/adjustment. Amount bounds apply to the signed amount. Returns the matching transactions (newest first) and a count.

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": "search_transactions"
  }
}

Input Schema

Name	Required	Description	Default
`text`	No
`limit`	No
`source`	No
`account`	No
`date_to`	No
`date_from`	No
`categories`	No
`max_amount`	No
`min_amount`	No
`entry_types`	No

Output Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Jul 8, 2026

TDQS

A4.2/5.0

Behavior4/5

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

No annotations are provided, so the description carries the full burden. It discloses that filters combine with AND, entry_types has specific allowed values, amount bounds apply to the signed amount, and results return newest first with a count. This adds significant behavioral context beyond the empty schema descriptions.

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 short sentences with clear structure. The first sentence states the purpose, and the second provides key details in a bullet-like format. Every sentence adds value without redundancy.

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 the tool has 10 parameters, no schema descriptions, and an output schema, the description covers the essential behavioral aspects (AND combination, entry_types, amount bounds, ordering, count). It lacks details on some parameters but leverages the output schema to convey return structure, resulting in good overall completeness.

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?

Schema description coverage is 0%, so the description must compensate. It only explains entry_types values and the fact that amount bounds apply to signed amount. Other parameters (text, source, account, dates, categories, limit) are not described. This adds partial meaning but is insufficient for 10 parameters.

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 it searches ledger transactions with flexible filters. The verb 'search' combined with the resource 'ledger transactions' is specific. It distinguishes from siblings by emphasizing filtering capability, which is not present in tools like sum_transactions or get_account_balance.

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

Usage Guidelines4/5

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

The description provides clear context on when to use the tool: for flexible filtering of transactions. It explains that filters combine with AND and lists acceptable values for entry_types, but does not explicitly mention when not to use or name alternatives. Given sibling tools have different purposes, the context is sufficient.

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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