schedule-iii

List allocation runs

list_allocation_runs

Read-only

MANAGEMENT data class. Discover persisted allocation runs and their conservation heads; this does not generate or recompute allocation. Money is decimal-string rupees. Results are ordered period, version, then run id, and the signed page_token is pinned to the complete filtered source: if it reports source_changed, restart at page 1. current_only means latest generated version, not source freshness; known_stale and not_assessed are both warnings, never a claim that the source is fresh. Raw stale reasons and warning context are not returned.

Input Schema

TableJSON Schema

Name	Required	Description
`period`	No	Optional management month in YYYY-MM.
`client_id`	Yes	The entity (client) id — from list_clients.
`page_size`	No	Rows per source-pinned page (default 20, max 50).
`page_token`	No	Signed continuation from the prior page; restart without it if source_changed.
`current_only`	No	Return only the latest generated version per period (default true); this is not a freshness claim.

TDQS

A4.7/5.0

Behavior5/5

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

Annotations already provide readOnlyHint=true, but the description adds substantial behavior beyond that: ordering by period/version/run id, page_token pinning and source_changed handling, current_only semantics (not freshness), stale warnings ('never a claim that the source is fresh'), and absence of raw reasons. This is rich context and fully transparent.

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

Conciseness5/5

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

Four dense sentences, each adding new information: classification, purpose, money format, ordering, pagination semantics, stale warnings, and exclusion of raw reasons. Front-loaded with purpose and no filler.

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?

Despite no output schema, the description fully covers the operational semantics: ordering, pagination with source pinning, current_only meaning, stale warning interpretation, and data format (decimal-string rupees). It is complete for a read-only list tool and handles edge cases.

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?

All 5 parameters are fully described in the schema (100% coverage), so the baseline is 3. The description elevates this by adding deeper meaning for page_token ('pinned to the complete filtered source... restart if source_changed') and current_only ('latest generated version, not source freshness'), which goes beyond the schema's descriptions.

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 pair ('Discover persisted allocation runs') and immediately clarifies scope ('conservation heads') while explicitly stating what it does not do ('does not generate or recompute allocation'). This strongly distinguishes it from generation tools like generate_schedule_iii.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

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

It clearly indicates when to use (discover persisted runs) and includes an explicit exclusion ('does not generate or recompute allocation'), but it does not name alternative tools or provide broader when-to-use guidance beyond this scope. The context is clear but not exhaustive.

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.

Resources

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