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cavishal04

TallyPrime MCP

by cavishal04

get_trial_balance

Get a trial balance from TallyPrime showing non-zero ledger closing balances in debit/credit columns with totals. Specify company and date range for filtering.

Instructions

Get a trial balance: every ledger with a non-zero closing balance, split into debit/credit columns, with totals.

Args: company: Company name. If omitted, uses the configured default. from_date: Start date, ISO format YYYY-MM-DD. Currently informational only — closing balances reflect TallyPrime's current ledger state rather than a point-in-time reconstruction; see docs/tools.md. to_date: End date, ISO format YYYY-MM-DD. Same caveat as from_date.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
companyNo
to_dateNo
from_dateNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.4/5.0
Behavior4/5

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

With no annotations provided, the description carries the full burden. It discloses a key behavioral trait: closing balances reflect TallyPrime's current ledger state rather than a point-in-time reconstruction, and points to docs/tools.md for more. It also states the output structure (debit/credit columns, totals). It could add more about permissions or side effects, but for a read-only report tool this is solid.

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 compact and front-loaded: the core purpose is in the first sentence, followed by a concise parameter list. Every sentence earns its place, and the caveat is clearly flagged.

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 an output schema and only three optional parameters, the description covers the essential behavior and parameter semantics. The main gap is not explicitly stating when to prefer this over get_ledger or get_profit_and_loss, but the scope is clear enough for an agent to select it correctly.

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 description coverage is 0%, so the description must compensate. It does: it explains the company parameter's default behavior and gives format and caveats for from_date and to_date. It doesn't detail exact date handling beyond the caveat, but it adds meaningful semantics beyond the bare 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 ('Get') and resource ('a trial balance'), and precisely defines the scope: every ledger with a non-zero closing balance, split into debit/credit columns, with totals. This clearly distinguishes it from sibling tools like get_ledger or get_profit_and_loss.

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 explains the role of each date parameter and explicitly warns that dates are informational only, which is important usage context. It does not explicitly name alternatives or when-not-to-use, but the clear scope and sibling list make the intended use evident.

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