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Get promotions

get_promotions
Read-only

List promotions (e.g. '2 for 30₪', club-member price, second-unit discount), optionally filtered by store_id or product_id, and by active=true to only return promotions currently running. Use this to explain why an optimize_delivery line price is lower than list_price.

Input Schema

NameRequiredDescriptionDefault
cityNoCity name in Hebrew or English (also accepts CBS locality codes). Restricts to promotions at stores in that city plus chain-wide promotions of chains present there.
limitNoMax promotions to return, ordered by soonest end date. Defaults to 50, max 200.
activeNoIf true, only currently-active promotions. Defaults to true.
store_idNoFilter to promotions at this store.
product_idNoFilter to promotions covering this canonical product.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.9/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=true, openWorldHint=true and destructiveHint=false, so the safety profile is covered. The description adds the semantic point that promotions explain discounted line prices, but says nothing about result ordering, pagination, or data freshness beyond what the schema already states.

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?

Two sentences with the resource and filters front-loaded; the parenthetical examples earn their space by clarifying promotion kinds. Slight redundancy with schema-documented filters keeps it just below a perfect score.

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?

No output schema exists, so the description bears some burden for return semantics; the parenthetical promotion examples and the list_price linkage give an agent enough to interpret results. It could still say more about the shape of returned promotions, but combined with full schema coverage and read-only annotations it is adequately complete.

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 description coverage is 100% and parameters are well documented there (city semantics including chain-wide promotions, limit defaults/max, active default). The description merely restates store_id, product_id and active, adding no syntax or interaction detail beyond the schema, so baseline 3 applies.

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?

States a specific verb and resource ('List promotions') and immediately grounds it with concrete examples of promotion types ('2 for 30₪', club-member price, second-unit discount). The cross-reference to optimize_delivery line price vs. list_price lets an agent place this tool relative to a sibling without opening any schema.

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?

Gives a clear trigger scenario: use it to explain why an optimize_delivery line price is below list_price, and names the sibling involved. It stops short of stating when NOT to use it or naming alternative lookup paths (e.g. get_product), so it is strong context without full exclusion guidance.

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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