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samarpassey

maple-procure

by samarpassey

summarize_spend

Rank total disclosed contract value by buyer, supplier, or category to identify top spenders, with transparent notes on excluded amounts and currencies.

Instructions

Rank total disclosed contract value by buyer, supplier or category. Use for "who spends most on X", "which suppliers earn the most", or comparing buyers over a period.

For the individual contracts behind a total, use search_awards.

Two things make every total a floor rather than a true total, and both are reported on the result: awards that disclosed no amount are excluded rather than counted as zero, and only one currency is summed per call because the source mixes CAD, USD and EUR with no conversion. The notes field states both in words — repeat them when you report a figure. A total presented as complete is wrong.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
sinceNoOnly awards dated on or after this date, as YYYY-MM-DD.
top_nNoHow many groups to rank.
untilNoOnly awards dated on or before this date, as YYYY-MM-DD. The whole day counts.
currencyNoThe single currency to total, e.g. 'CAD' (the default and 92% of rows), 'USD', 'EUR', or 'unspecified' for awards naming no currency. Amounts are never converted or combined. Every result reports the period's full currency split so you can see what was left out.CAD
group_byNoWhat to rank. 'buyer' for the government department awarding the contracts, 'supplier' for the company winning them, 'category' for the kind of procurement.buyer

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior5/5

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

With no annotations, the description fully carries the behavioral disclosure burden. It transparently explains the two major caveats: undisclosed awards are excluded (not counted as zero) and only one currency is summed per call, plus instructs the agent to repeat the `notes` field when reporting figures.

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?

The description is front-loaded with purpose and examples, then delivers critical caveats in a compact second paragraph. Every sentence adds value; there is no filler or redundant restating of schema information.

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 having an output schema, the description covers all needed decision-making context: use cases, alternatives, data caveats, and reporting instructions. It fully prepares the agent to use the tool correctly without needing external clues.

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?

The schema already has full descriptions for all 5 parameters, so the baseline is 3. The description adds meaningful extra semantics, particularly around the `currency` parameter (source mixes CAD/USD/EUR with no conversion, default is CAD, amount never combined) and group_by behavior, elevating it above 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 clearly specifies a concrete action: 'Rank total disclosed contract value by buyer, supplier or category,' immediately identifying the resource and capabilities. It also distinguishes itself from siblings by explicitly pointing to `search_awards` for individual contract detail.

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?

The description provides direct usage guidance with concrete example queries ('who spends most on X', 'which suppliers earn the most') and explicitly contrasts the tool with `search_awards` for 'individual contracts behind a total'. This gives clear when-to-use and when-not-to-use context.

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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