schedule-iii

Show one upload’s figures and get the approval

preview_chart_rebaseline

Show ONE upload’s figures as they stand right now, and get the approval confirm_complete_chart needs. Call this after list_chart_rebaselines, for the one upload your user is considering. It reports the same counts the list does — how many distinctive ledger names Datavrn already treats as evidence (trusted_considered), how many this upload adds (incoming_considered), how many are on both sides (matched), the two coverage ratios, and which direction fell short (shortfall) — re-read at this moment rather than when you listed. Read them to your user in their own terms and ask them plainly whether that upload is the entity’s whole book now. NEVER decide this from the numbers yourself — a large jump, a round number or a long gap is a reason to ASK, never a reason to conclude. Only the person who knows the client’s books can answer it. The approval is single-use, expires in 15 minutes, and is tied to this entity, this upload, the member you name, and the exact figures returned here. If the entity’s uploads change in between — including a later clean file that resolves this on its own — the approval is spent and you start again from this call. Datavrn refuses and changes nothing if this upload is not awaiting a complete-chart confirmation: it was never held out of the evidence, someone already confirmed it, or it is one Datavrn asked about directly — for that last one, answering that question is what settles it.

Input Schema

TableJSON Schema

Name	Required	Description
`client_id`	Yes	The entity (client) id — from list_clients.
`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.
`source_snapshot_id`	Yes	The upload’s id — the `snapshot_id` field of a list_chart_rebaselines row.

TDQS

A4.6/5.0

Behavior5/5

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

Beyond the annotations (readOnlyHint=false, destructiveHint=false), the description discloses critical side effects: the approval is single-use, expires in 15 minutes, is tied to entity/upload/member/figures, and is spent if uploads change between the call and the confirmation. It also details refusal conditions that leave state unchanged, which is essential for an agent to predict the tool's impact.

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 every sentence contributes: purpose, usage, return values, side-effect disclosure, and refusal conditions are all necessary for correct invocation. It is front-loaded with the core purpose and then expands logically. Could be tightened slightly, but the length is justified by the tool's complexity.

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 fully specifies the returned data (the five counts and two ratios) and how it relates to the list_chart_rebaselines output ('re-read at this moment'). It also covers the approval's constraints and refusal conditions, so an agent has everything needed to call it correctly and understand its effects.

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 description coverage is 100%, so the schema already documents client_id, source_snapshot_id, and on_behalf_of. The description adds only light context (e.g., 'the one upload your user is considering' and the approval's tie to the member named), but does not clarify parameter syntax or edge cases beyond the schema. Baseline of 3 is appropriate since the schema carries the detail.

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 clearly states the verb (show) and resource (ONE upload’s figures), and explicitly ties it to the approval that confirm_complete_chart needs, distinguishing it from siblings like list_chart_rebaselines and confirm_complete_chart. It names the specific precondition (after list_chart_rebaselines) and the entity of interest, leaving no ambiguity about the tool's role.

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?

Provides explicit when-to-use ('Call this after list_chart_rebaselines, for the one upload your user is considering') and when-not-to-use (refusal conditions: not awaiting confirmation, already confirmed, or a directly asked upload). It also instructs the agent to never decide based on numbers alone and to ask the user, giving clear behavioural guidance for selection.

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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