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procurement_watch

Monitor purchasing health by identifying late receipts and stale RFQs, then receive a verdict on procurement status.

Instructions

Report purchasing health — late receipts and stale RFQs — in one call.

Composes confirmed purchase orders into open value, receipts past their planned date, per-vendor open spend, plus a count of quotation requests (draft/sent) older than rfq_stale_days, and a rule-based verdict.

Args: late_grace_days: Days past date_planned before a receipt counts as late (default 0). rfq_stale_days: Age in days after which a draft/sent RFQ counts as stale (default 7). top_n: Rows in the late-receipts / top-vendors lists (default 5). timezone_offset: UTC offset for "today" (default 7 = Asia/Ho_Chi_Minh). company: Optional company name (ilike) or id to scope the report.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
top_nNo
companyNo
rfq_stale_daysNo
late_grace_daysNo
timezone_offsetNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior3/5

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

The description lists what the report composes (open value, late receipts, per-vendor spend, stale RFQs, rule-based verdict), which provides behavioral context. However, since no annotations are provided, it does not explicitly state that it is read-only or disclose any side effects, but the composition details are sufficient for an agent to understand the output.

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 well-structured with a clear summary, composition details, and an Args section. It is efficient but slightly verbose; could be trimmed slightly while retaining clarity.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the complexity of the tool (5 parameters, no annotations, output schema exists), the description provides a thorough overview of what the report includes. It adequately covers the return structure without needing to detail output format since an output schema exists.

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?

With 0% schema description coverage, the description compensates well by explaining each parameter's purpose and default values in the Args section (e.g., late_grace_days, rfq_stale_days, top_n, timezone_offset, company). This adds meaning beyond the schema.

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 it is a 'Report purchasing health — late receipts and stale RFQs — in one call.' This specific verb+resource combination distinguishes it from sibling health reports like inventory_risk or production_health.

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

Usage Guidelines3/5

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

The description implies usage for a consolidated purchasing health check but does not explicitly state when to use this tool versus alternatives or provide any exclusions. No guidance on when not to use it.

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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