schedule-iii

List what automatic syncs replaced

list_replacements

Read-only

List what a connected data source’s AUTOMATIC syncs have REPLACED for one entity — each one showing what was overwritten, how many records, and whether it can still be undone. Call this when a user says figures for a past period look wrong or changed on their own, and after any surprise in a period a connector covers. Datavrn keeps the replaced data for 30 days from the moment it was replaced: within that window state is "restorable" and preview_replacement_restore/restore_replacement can put it back; after it, state is "lapsed" — the record of what happened is kept and is still listed here, but it can no longer be undone from this connection, so tell the user to contact Datavrn support if they need that data recovered. "restored" means it has already been put back. Only syncs that ran UNATTENDED are listed: a replacement someone on the team previewed and confirmed themselves is not offered for undo, by design. window_closes_at is the date the undo window shuts. If connection_attributed is false, Datavrn can no longer prove which connection made that replacement — say so rather than naming this one. To find these without a connection id, call get_pending_work: it lists every still-undoable replacement across the entities you can see, with the entity and connection to use here.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`client_id`	Yes	The entity (client) id — from list_clients.
`connection_id`	Yes	The connected data source’s id — from get_pending_work’s restorable_replacements rows, or from the entity’s data-sources page in the web app.

TDQS

A4.8/5.0

Behavior5/5

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

Annotations already declare readOnlyHint=true and destructiveHint=false. The description adds substantial behavioral context: 30-day retention window, state transitions (restorable/lapsed/restored), the unattended-only filter, the connection attribution caveat, and the semantics of window_closes_at. This goes far beyond the annotations and is essential for correct use.

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 carries information. It front-loads the core purpose, then states lifecycle, exceptions, and alternatives. For the complexity (state machines, undo windows, attribution caveats), the length is justified. Slight structure improvements could split into paragraphs, but it's coherent and efficient.

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?

Given no output schema, the description explains the return contents (what was overwritten, record counts, undoability) and all state values. It covers the full workflow: when to call, what to expect, and what to do afterwards (preview/restore or support). Nothing an agent needs is missing.

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 coverage is 100% with descriptions for both parameters. The description adds provenance guidance: client_id comes from list_clients, connection_id from get_pending_work or the web app. This is valuable, though not exhaustive—it doesn't explain format or edge cases. Still, it elevates beyond the schema baseline.

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 ('list') and resource ('what a connected data source’s AUTOMATIC syncs have REPLACED for one entity') and enumerates what each entry shows. It clearly differentiates from siblings like get_pending_work by naming it as the alternative for cross-entity discovery, and preview_replacement_restore/restore_replacement as follow-up actions.

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 states when to call: 'when a user says figures for a past period look wrong or changed on their own, and after any surprise in a period a connector covers.' It also gives a direct when-not: manual confirmations are excluded by design, and provides an alternative (get_pending_work) for the case without a connection id. No ambiguity.

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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