Skip to main content
Glama
Sign In
monarch-mcp

monarch-mcp

by vargahis
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

get_transactions_summary

Summarize all transactions with key metrics: count, sum, average, maximum, income, and expenses.

Instructions

Get aggregate transaction summary (count, sum, avg, max, income, expenses).

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": "get_transactions_summary"
  }
}

Input Schema

NameRequiredDescriptionDefault

No arguments

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

B3.1/5.0
Behavior2/5

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

With no annotations, the description carries the full burden of behavioral disclosure. It only states the tool gets a summary, but omits details like data scope (e.g., time period, account filtering), return behavior (e.g., empty result handling), or potential side effects (none expected). Minimal transparency.

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 sentence that efficiently conveys the core purpose and included metrics. No extraneous words.

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?

The output schema exists, so return structure is covered. However, the description fails to specify the data context (e.g., which transactions are summarized, over what period). For a tool that aggregates data, this missing context leaves the agent uncertain about its applicability.

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 input schema has no parameters, so schema coverage is effectively 100%. The description adds value by enumerating the aggregate fields returned, which is the main semantic content beyond the empty schema.

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 that it gets an aggregate transaction summary and lists the specific aggregates (count, sum, avg, max, income, expenses). However, it does not differentiate itself from sibling tools like get_cashflow_summary or get_aggregate_snapshots, missing a chance to clarify its unique scope.

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?

No guidance is provided on when to use this tool versus alternatives (e.g., get_transactions for detailed records, get_cashflow_summary for cash flow metrics). The description leaves the AI agent to infer usage context without any hints.

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

Deploy Server

Other Tools