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guru_coupons_write_update

Mutações em cupons Guru. Ações: create (data); update (id + data); delete (id); duplicate (id); set_activation (id + data, ativa/desativa).

[Flattened action: update]

Bulk support: accepts ids for batched execution.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
idNo
idsNo
dataNo
accountNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.5/5.0
Behavior3/5

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

Annotations only declare readOnlyHint=false, leaving the safety profile undeclared. The description adds value by revealing this is a multi-action mutator that can also delete and activate/deactivate coupons, and that it supports batched writes. However, it doesn't disclose side effects, idempotency of retries, or whether batched execution is atomic — notable gaps for a tool that can trigger deletes.

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 compact — a one-line summary, a terse action list with per-action parameter shapes, and a two-word bulk note — with no wasted filler. It loses one point because the five actions are tightly packed into a parenthetical list that blends action semantics with transport detail, making it slightly dense to parse at a glance.

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

Completeness2/5

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

Given there is no output schema and no informative annotations, the description must explain return behavior, error semantics, and the meaning of the 'data' structure, but it doesn't. The note that this operation can mutate and even delete resources heightens the need to document failure modes and atomicity, which is absent — leaving a destructive, batched tool under-specified.

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?

With 0% schema coverage, the description carries the full burden and does compensate meaningfully: it maps id/data to specific actions and explains that 'ids' enables bulk execution. However, the 'account' parameter is completely undocumented, and the 'data' structure for set_activation (explicitly noted as ativa/desativa) is only hinted at. Partial but useful compensation for a fully undocumented 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 identifies this as 'Mutações em cupons' (coupon mutations) and explicitly enumerates the five actions (create, update, delete, duplicate, set_activation) plus bulk execution. The '[Flattened action: update]' note and mapping of actions to parameter shapes (id vs id+data) distinguish it from sibling tools like guru_coupons_write_delete and guru_coupons_write_duplicate, making the resource and scope unambiguous.

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 gives usage context by listing which parameters each action requires and noting bulk support ('accepts ids for batched execution'), which implies when to use ids vs singular mutations. However, it never explicitly names sibling alternatives or states when NOT to use this tool (e.g., for create/delete you should call the specific sibling), leaving usage inference implicit.

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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