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paid-media-mcp

paid-media-mcp

by kenlim5656
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
Hybrid

compare_attribution_models

Compare two attribution configurations side-by-side to reveal differences in model type, windows, conversion events, and use cases.

Instructions

Compare two attribution configurations side-by-side to understand how they differ in model type, windows, conversion events, and use cases.

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": {
      "attribution_id_a": "<attribution_id_a>",
      "attribution_id_b": "<attribution_id_b>"
    },
    "name": "compare_attribution_models"
  }
}

Input Schema

NameRequiredDescriptionDefault
attribution_id_aYesFirst attribution configuration ID
attribution_id_bYesSecond attribution configuration ID

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv2.0.0

TDQS

B3.4/5.0
Behavior2/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions comparison but does not state whether the tool is read-only, if it requires any special permissions, what the output contains, or any side effects. This is insufficient for a tool with no annotations.

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 a single sentence that is front-loaded with the core action and outcome. No redundant words or unnecessary details are present; every part serves the purpose.

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 absence of an output schema, the description should at least hint at the return format (e.g., side-by-side comparison table, list of differences). It only mentions what aspects are compared but not how results are presented. For a simple tool with two parameters, this lack of output context leaves the agent guessing.

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 descriptions already cover both parameters (attribution_id_a, attribution_id_b) fully, achieving 100% coverage. The description adds value by specifying the aspects compared (model type, windows, conversion events, use cases), but this is marginal and does not significantly enhance parameter understanding beyond the 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 states the tool's purpose: comparing two attribution configurations side-by-side to understand differences in model type, windows, conversion events, and use cases. It uses a specific verb ('compare') and resource ('attribution configurations'), effectively distinguishing it from siblings like get_attribution_model or list_attribution_models.

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 implies usage when a user wants to understand differences between two configurations, but it does not explicitly state when to use this tool versus alternatives (e.g., get_attribution_model for a single model). No exclusion criteria or context for decision-making is provided.

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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