schedule-iii

Read trial-balance source data

get_tb_rows

Read-only

Read the SOURCE DATA behind a statement: the trial-balance rows (account name, debit, credit) as landed for a period, BEFORE grouping — the pre-statement numbers, not statement figures. PREFER FILTERS over fetching everything: name_patterns (e.g. ['cash','bank','od']), side ('debit'/'credit' by net balance), and min_abs_balance return a small exact subset with its own debit/credit totals — e.g. wrong-side cash accounts = name_patterns ['cash','bank'] + side 'credit'. Paginated (page 1-based; page_size default 50, max 500). These are the CURRENT live rows: statement figures are frozen at a generated version, so if the trial balance was re-uploaded after a version was generated, these rows may not tie to that version (the response note says so). Amounts are decimal strings in rupees. Figures are Datavrn's deterministic engine output; interpretation is your assistant's.

Input Schema

TableJSON Schema

Name	Required	Description
`page`	No	1-based page number (default 1).
`side`	No	Keep only accounts whose net balance falls on this side (debit = debits exceed credits). Accounts netting to zero match neither.
`client_id`	Yes	The entity (client) id — from list_clients.
`page_size`	No	Rows per page (default 50, max 500 — prefer filters over big pages).
`period_id`	Yes	The reporting period id — from list_periods.
`name_patterns`	No	Up to 10 case-insensitive substrings; an account matches if its name contains ANY of them (e.g. ['gst','tds']).
`min_abs_balance`	No	Keep only accounts whose balance (the larger of its debit/credit) is at least this many rupees — a decimal string like '100000'.

TDQS

A4.9/5.0

Behavior5/5

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

Annotations already mark read-only and non-destructive, but the description adds valuable behavior: rows may not tie to a generated version after re-upload, amounts are decimal strings in rupees, output is deterministic, and pagination behavior. These go beyond annotations.

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 dense but every sentence earns its place: purpose, filtering guidance, pagination, data freshness caveat, amount format, and interpretation responsibility. It is well-structured and front-loaded.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With no output schema, the description covers the key output fields (account name, debit, credit, totals, note), filters, pagination, and data semantics. It is complete for an agent to decide and invoke 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 has 100% coverage, so baseline is 3. The description enhances this by providing examples for name_patterns, side, and min_abs_balance, plus clarifying pagination defaults, adding meaningful context 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+resource+scope: 'Read the SOURCE DATA behind a statement: the trial-balance rows (account name, debit, credit) as landed for a period, BEFORE grouping'. It clearly distinguishes from statement figures and sibling tools like get_statement_figures.

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

Usage Guidelines5/5

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

Explicitly says to 'PREFER FILTERS over fetching everything', gives a concrete example (wrong-side cash accounts), and notes the 'CURRENT live rows' vs frozen statement figures, indicating when not to use this tool. It also contrasts with statement figures.

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

All schedule-iii tools Inspect

Try in Browser

RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

A4.1/5.0

Disambiguation5/5

Each tool targets a distinct resource or action — get_* reads, save_* writes, confirm_* approves, preview_* shows consequences before approval. Even the management-data trio (budgets, allocations, variance) is cleanly separated by surface. Two-step flows like preview_chart_rebaseline → confirm_complete_chart are clearly sequenced, so an agent won't confuse the stages.

Naming Consistency5/5

Tool names follow a highly consistent verb_noun pattern: get_* for reads, list_* for discovery, save_* for section writes, confirm_* for approvals, create_* for new entities/centres, preview_* for pre-approval checks. The few one-offs (ingest_upload, upload_trial_balance, set_header_row) still fit the verb-first convention. No camelCase or style mixing.

Tool Count2/5

At 67 tools this is well past the 'too many' threshold. While the Schedule III domain genuinely is broad — statutorily mandated sections, two-phase approval flows, readiness checks, and a separate management-data area — the surface is heavy; an agent will spend real effort just surveying the tool list. Some consolidation of the save_reserves/provisions/assets movements or merging preview+confirm pairs is possible.

Completeness4/5

The surface covers the full lifecycle: upload → mapping/costing → grouping → capture (all statutory sections) → declarations → readiness → generate → finalise → download, plus entity setup and consolidated statements. Minor gaps: no tool directly exposes historical version diffing beyond list_snapshots, and the management-data section (budgets, allocations, variance) feels bolted on rather than integral to the core flow.

Resources

Need Help?