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finance-engines-mcp

finance-engines-mcp

by Cubiczan
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
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Parse trial balance

parse_trial_balance

Flatten a Xero trial balance payload into netted section balances (debit positive for assets/expenses, credit positive for revenue/liabilities/equity). Returns a dict keyed by section, mapping account name to balance.

Instructions

Flatten a Xero Reports/TrialBalance payload into netted section balances (debit positive for assets/expenses, credit positive for revenue/liabilities/equity). Returns a dict keyed by section, each mapping account name -> balance.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "report": "<report>"
    },
    "name": "parse_trial_balance"
  }
}

Input Schema

NameRequiredDescriptionDefault
reportYesA parsed Xero Reports/TrialBalance JSON payload.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.5/5.0
Behavior3/5

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

With no annotations, the description carries the full burden and does disclose the transformation logic, sign conventions, and return shape. However, it does not mention error behavior, handling of malformed or missing sections, or whether the input is validated. This is adequate but not exhaustive.

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 two compact sentences with no filler. The core action and output structure are front-loaded, and each sentence carries necessary information. It is appropriately concise for a single-parameter tool.

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?

Without an output schema or annotations, the description must explain both inputs and outputs fully. It covers the transformation and the returned dict structure, but omits usage context, possible section names, and failure behavior. It is sufficient for basic invocation but not fully complete for an agent operating autonomously.

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 100%, so the baseline is 3. The description's 'Xero Reports/TrialBalance payload' essentially repeats the schema's description of the 'report' property without adding new format constraints, examples, or edge-case semantics. It meets the baseline but adds no extra value beyond the schema.

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 states a specific verb ('Flatten'), an explicit resource ('Xero Reports/TrialBalance payload'), and the exact output (netted section balances with sign conventions). This clearly distinguishes it from its financial-analysis siblings like price_sensitivity, breakeven, or compute_covenant_metrics.

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 given on when to use this tool versus alternatives, nor what prerequisites the input must satisfy. The intended use case is implied by the name and domain, but there is no explicit when/when-not/alternative routing.

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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