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company_probe_outcomes

Evaluate price probe results against margin and volume targets to determine if each SKU move applied within tolerance, then provide verdicts and next actions.

Instructions

Score a sealed price probe table against the margin report of the window after (built now from the persisted commerce chains): per probed SKU whether the move was applied within tolerance, the volume change a day against the break-even volume change allowed, the contribution a day before and after, and a verdict (held, backfired, not applied, unmeasured) with the next step (payload: probes, and optionally window_start, window_end, before, previous_window_start).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nowNo
engineNo
operationNobuild
entity_refNo
project_idYes
bundle_jsonYes
payload_jsonNo{}

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv0.1.2

TDQS

C2.4/5.0
Behavior2/5

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

No annotations are provided, so the description carries the burden. It discloses that it builds the margin report from persisted commerce chains, but does not state whether the operation is read-only or has side effects. It mentions a 'next step' but does not clarify if it triggers an action. This ambiguity is a significant gap.

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

Conciseness2/5

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

The description is a single run-on sentence, dense but poorly structured. It front-loads the main action but then crams numerous output fields and a payload list without clear organization, making it hard to parse.

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 the tool's complexity, zero schema coverage, and no annotations, the description is inadequate. It does not explain the required parameters, does not clarify the output structure (though an output schema exists), and omits context about when the probe is sealed or how to prepare inputs. The agent would struggle to call this correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters1/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must explain parameters. It mentions 'probes' and optional window fields, but these are not the input schema properties (which include project_id, bundle_json, now, engine, operation, entity_ref, payload_json). It fails to explain the required project_id and bundle_json, leaving the agent guessing.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific action (score) and resource (sealed price probe table) and describes the output details: per-SKU application within tolerance, volume change vs break-even, contribution before/after, verdict, and next step. It is fairly specific and distinct from siblings like company_price_probes, though it doesn't explicitly differentiate.

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

Usage Guidelines2/5

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

No guidance on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., a sealed probe table must exist) or conditions for selection. The description only explains what it does, not when to invoke 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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