Skip to main content
Glama

schedule-iii

Save regulatory affirmations

save_regulatory_affirmations
Destructive

Save the CARO / Other Regulatory Information affirmations your user has confirmed, one per title. SEND THE COMPLETE SET EVERY TIME: this replaces the whole Other Regulatory Information note, so any title you leave out of this call is removed — including one someone answered in the Datavrn app. Call list_statement_policy_choices first and send back every title. If your call would drop a saved affirmation, Datavrn saves nothing and returns an approval request naming how many would be dropped — show your user, and send the approval back only if they mean to drop them. A complete resend drops nothing and saves straight away. Use the exact affirmation headings this statement format carries; a heading Datavrn does not recognise is refused and nothing is saved. Some statement formats — the ICAI formats for LLPs and non-corporate entities — carry no Other Regulatory Information note at all, and this tool refuses for them. Each affirmation is a REGULATORY REPRESENTATION made in the entity’s name — for example whether any proceedings for benami property are pending, or whether the entity has been declared a wilful defaulter. Put each one to your user individually and record their answer. Never affirm one because it is the usual answer, never infer one from a template default, and never confirm a batch of them in one go. If an affirmation differs from last year’s answer, tell your user — call list_statement_policy_choices to see what was answered last year. Saving here re-opens the disclosure review — after your last change, confirm the disclosure review again with confirm_capture_review before generating. Recorded as authorised by the member you name. Generate a fresh version after your last capture change — finalisation checks the version’s frozen capture state, not today’s.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
client_idYesThe entity (client) id — from list_clients.
period_idYes
template_idYes
affirmationsNo
on_behalf_ofNoThe 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.
removal_countNo
removal_tokenNo

TDQS

A4.5/5.0
Behavior5/5

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

Annotations already mark destructiveHint=true, but the description goes far beyond: it explains the replace-all behavior, the approval request for dropped affirmations, refusal on unrecognised headings, and side effects like reopening the disclosure review. No contradiction with annotations.

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

Conciseness3/5

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

The description is thorough but verbose, with repeated instructions (list_statement_policy_choices appears twice) and examples that could be condensed. While the length is somewhat justified by the destructive and complex nature of the tool, the redundancy prevents a higher score.

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?

The description covers the full workflow for a complex, destructive tool: preconditions, replacement semantics, approval and refusal paths, user consent rules, and post-save actions (reopen disclosure review, generate a fresh version). Despite no output schema, it gives enough context for correct invocation.

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 only 29%, so the description must compensate. It explains affirmations must be the complete set with exact headings and one per title, and it indirectly covers removal_count/removal_token through the approval flow, but does not explicitly mention these parameters. client_id, template_id, and period_id are not described beyond the schema, leaving some gaps.

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 tool saves CARO / Other Regulatory Information affirmations, one per title, and explicitly says it replaces the whole note. This specific verb+resource distinguishes it from sibling save_* tools like save_disclosures and save_accounting_policies.

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?

It gives explicit pre-requisites: 'Call list_statement_policy_choices first and send back every title.' It also gives post-requisites: 'confirm the disclosure review again with confirm_capture_review before generating.' It names exclusions: 'the ICAI formats for LLPs and non-corporate entities — carry no Other Regulatory Information note at all, and this tool refuses for them.'

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

Try in Browser

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