schedule-iii

Check finalisation state and get approval

get_finalise_readiness

Read the full finalisation state of one statement version, and get the approval finalise_statement needs. Call it ONCE immediately before finalising — it re-reads the stored workbook, so do not poll it. SHOW YOUR USER EVERY ROW THIS RETURNS — the gates that must be green, each warning they would be accepting and why, how many input cells are still empty, any control that could not be evaluated, and capture_live_diverged_message when it is present — before you finalise. Do not summarise the warnings away. capture_live_diverged_message means a capture answer changed after this version was generated: the version can still be sealed as it stands, and generating a fresh one is the alternative. Read it out and let your user choose. A control that "could not be evaluated" is not a pass: it is a check Datavrn did not run, and your user is entitled to know what was not checked before they seal the version. The approval is single-use, expires in 15 minutes, and is tied to this exact version, this connection and the member you name — if anything about the statement changes in between, finalise_statement will refuse and you start again from here. If your connection is not permitted to finalise, you still get the whole state; there is simply no approval to hand back.

Input Schema

TableJSON Schema

Name	Required	Description
`client_id`	Yes	The entity (client) id — from list_clients.
`snapshot_id`	Yes	The statement version id — from list_snapshots or get_schedule3_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

A4.6/5.0

Behavior5/5

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

The description reveals important behavioral traits beyond the annotations: it re-reads the stored workbook (explaining why polling is discouraged), the approval is 'single-use, expires in 15 minutes, and is tied to this exact version, this connection and the member you name,' and it clarifies that non-permitted connections still get the full state but no approval. This adds significant context that the annotations do not convey.

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 long but each section serves a purpose: usage timing, user display requirements, side-effect caveats, and permission behavior. It is front-loaded with the core purpose and the key warning. The length is justified by the tool's complexity, though it could be tightened slightly.

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?

There is no output schema, so the description must explain what the tool returns. It details every row type (gates, warnings, empty input cells, unevaluated controls, capture_live_diverged_message) and explains their meanings. It also covers the full lifecycle from call to finalise and the failure mode if the state changes, making it complete for this complex tool.

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?

The schema provides descriptions for all three parameters (100% coverage), including format hints and which are required. The description does not add specific parameter-level semantics beyond referencing 'the member you name' and 'your user', so the baseline of 3 is appropriate.

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 opens with a specific verb+resource: 'Read the full finalisation state of one statement version, and get the approval finalise_statement needs.' This clearly states what the tool does and differentiates it from the sibling finalise_statement by positioning it as the prerequisite readiness check. The title also reinforces the purpose.

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?

The description gives explicit timing: 'Call it ONCE immediately before finalising — it re-reads the stored workbook, so do not poll it.' This provides a clear when-to-use instruction and an exclusion (do not poll). It also explains the consequence of stale state ('finalise_statement will refuse'), which guides the agent on when to re-run the tool.

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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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.

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