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snw-derived-mcp

snw-derived-mcp

by EvanSener
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

snw_execute_derived_property

Execute a registered derived property on local Amazon Ads mock data to compute property values for specified target objects and object IDs.

Instructions

在本地 Amazon Ads mock 数据上执行已注册派生属性。

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "target_object": "<target_object>",
      "property_api_name": "<property_api_name>"
    },
    "name": "snw_execute_derived_property"
  }
}

Input Schema

NameRequiredDescriptionDefault
object_idsNo
target_objectYes
property_api_nameYes

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

C2.8/5.0
Behavior2/5

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

Annotations are absent, so the description carries full disclosure burden. It does reveal a prerequisite (the property must already be registered) and the local mock scope, but it does not say whether execution mutates state, computes and returns values, or fails when the property is absent.

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 a single, front-loaded sentence with no filler or redundancy. It is concise, though its brevity leaves substantive gaps addressed in other dimensions.

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?

With no annotations and very little description, the tool is under-specified for the agent to invoke confidently. It lacks parameter meaning, usage direction, and behavioral expectations; the existence of an output schema covers return format but not call context.

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% and the description does not explain any of the three parameters. target_object, property_api_name, and object_ids are named but not semantically defined, so the agent gets no added meaning beyond the raw field titles.

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 specifies a clear action ('execute'), a precise resource ('registered derived property'), and a scope ('local Amazon Ads mock data'). This contrasts with sibling tools named create_derived_property and list_derived_properties, making the tool's purpose distinguishable without opening the schema.

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?

There is no explicit when-to-use guidance, no exclusions, and no mention of alternatives. 'Local mock data' implies a test/dev context, but the agent is not told when to select this tool over create/list/analyze siblings.

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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