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gl_trial_balance

Per-account debit/credit columns for one fiscal year, with totals.

fiscal_year (year label) defaults to the latest year posted in; period_id narrows to one period of it. No all-years mode — account identity is (company, fiscal year, code), so aggregating double-counts silently (ZET-155). Unknown year/period → 404; period outside the year → 422.

Cut by the line's stamped fiscal_year_id, not its posting date; …/financial-statements cuts by posting date inside the same year's stored window, so the two differ only on a line dated outside the year it is stamped to. fiscal_year_cut names the cut (ZET-166).

Input Schema

NameRequiredDescriptionDefault
period_idNo
company_idYes
fiscal_yearNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.9/5.0
Behavior4/5

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

With no annotations, the description carries the full burden and delivers: default resolution, 404 vs 422 error semantics, the double-counting hazard, and the fiscal_year_id vs posting-date cut distinction. It stops short of auth/permission needs or rate limits.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The core scoping statement is front-loaded and dense with useful facts, but the final paragraph about cut semantics is convoluted, and internal ticket references (ZET-155, ZET-166) are noise that consume space without helping an agent invoke the tool.

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?

With no output schema and no annotations, the description supplies the return shape at a high level (columns plus totals), error behavior, and cut semantics. It is nearly sufficient, missing only detail on the exact column set and whether totals are included per account or only overall.

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?

Schema coverage is 0%, so the description must compensate and largely does: fiscal_year is a year label defaulting to the latest posted year, period_id narrows within that year, and account identity is the (company, fiscal year, code) tuple. It never states the accepted type/format for company_id or period_id, leaving some gaps for a 0%-coverage 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 opening clause names a specific verb/resource pair: per-account debit/credit columns for one fiscal year with totals. It differentiates itself from the financial-statements path by cut semantics, but does not address how it differs from gl_balance_sheet or get_gl_account_ledger, which an agent could plausibly confuse with a trial 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?

It explains that fiscal_year defaults to the latest posted year, that period_id narrows to one period, and explicitly warns there is no all-years mode because aggregation double-counts. It also names a contrasting path (…/financial-statements) and when the two diverge. No explicit 'use this instead of X' routing to the other GL tools.

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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