schedule-iii

Check the previous-year comparative source

get_comparative_source_state

Check whether this statement’s previous-year comparative can be sealed, and get the approval assert_previous_year_no_activity needs. Datavrn refuses to finalise a statement whose previous-year figures come from a trial balance drawn AFTER the year-end closing entries: that derives a previous-year Profit and Loss of all zeroes which foots perfectly and is not last year’s results. When post_closing_detected is true, READ THE WHOLE finding TO YOUR USER — what the state is and all three ways out — and let them choose. Never choose for them. Two of the three remedies are things only they can do (upload the pre-closing trial balance, or enter last year’s signed figures as previous-year values, then generate a fresh version). The third is an assertion that the previous year genuinely had NO ACTIVITY, which is a statement about their client’s accounts, in their words, recorded in their name — a dormant company is the case it exists for. The approval is single-use, expires in 15 minutes, and is tied to this statement, this connection and the member you name; if the previous-year figures change in between, the assertion will be refused and you start again from here. If nothing is wrong there is no approval to hand back, because there is nothing to assert.

Input Schema

TableJSON Schema

Name	Required	Description
`client_id`	Yes	The entity (client) id — from list_clients.
`period_id`	Yes	The reporting period id — from list_periods.
`template_id`	Yes	The statement template id (e.g. 'schedule3_v1' Division I; see list_snapshots/workspace).
`on_behalf_of`	No	The email address your user signs in to Datavrn with. This records who authorised the change alongside the connection that made it. REQUIRED on an API-key connection — ask your user for it, do not guess. On an OAuth connection leave it out: the change is recorded as authorised by the member who connected; if you do supply it, it must be that member.

TDQS

A4.5/5.0

Behavior5/5

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

Annotations only provide readOnlyHint=false and destructiveHint=false, so the description carries the transparency burden. It fully discloses side effects: the approval is single-use, expires in 15 minutes, tied to statement/connection/member, and can be refused if figures change. It also explains that this tool can return a post_closing_detected flag and instructs the agent to report the full finding to the user, which is a behavioral mandate.

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

Conciseness4/5

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

The description is ~140 words, long but densely packed with necessary safety-critical information. It front-loads the core purpose, then explains the problem, remedies, and constraints in a coherent flow. No redundancy, but it is long; however, given the complexity and the need to instruct the agent on handling the finding, it is justified. Slightly exceeds ideal conciseness but earns its length.

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?

The description explains the output indirectly (post_closing_detected flag, approval object) and the exact actions the agent must take. It covers the main scenarios (post-closing detected, nothing wrong) and all constraints (expiry, ties, refusal). No output schema exists, so the description must compensate, and it does so well, though it doesn't enumerate the full return structure (e.g., whether it returns the three remedies explicitly). Minor gap, but sufficient for correct usage.

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 coverage is 100% with detailed descriptions for each parameter (uuid formats, template examples, on_behalf_of email rules). The tool description does not add meaning beyond the schema; it focuses on behavior and workflow rather than parameter specifics. Baseline 3 is appropriate as the schema already covers semantics.

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?

Purpose is crystal clear: 'Check whether this statement’s previous-year comparative can be sealed, and get the approval assert_previous_year_no_activity needs.' It names the specific verb, resource, and ties to the sibling tool. The context about the all-zeroes P&L problem distinguishes it from other tools like save_py_values or upload_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 Guidelines5/5

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

Explicitly tells the agent when to use it (when previous-year figures come from a post-closing trial balance) and what to do: read the whole finding, let the user choose among three remedies, never choose for them. It also states when no approval is needed. This is exemplary usage guidance, going beyond just 'when to use' to provide a decision tree.

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?