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pops4-mcp-catalog

by io.github.HuangGoodmanAgency
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get_revenue_attribution

Return recorded pipeline impact for an event id where the dashboard holds it; otherwise return the attribution model and how it is tracked, with a link. Never reports a fabricated figure.

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": {
      "event_id": "<event_id>"
    },
    "name": "get_revenue_attribution"
  }
}

Input Schema

NameRequiredDescriptionDefault
event_idYesEvent id

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.2/5.0
Behavior4/5

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

With no annotations, the description carries the behavioral disclosure burden. It discloses the conditional fallback behavior and explicitly promises never to fabricate a figure, which is valuable reliability context. It does not mention permissions, errors, or data freshness, but these are less critical for a simple lookup.

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 two sentences with no filler. The primary behavior is front-loaded, the fallback is presented second, and the honesty guarantee earns its place as a final constraint.

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?

For a one-parameter lookup with no output schema, the description covers the main outcome and the alternate outcome, plus a key reliability caveat. It could add a bit more detail about what the link points to or what 'tracked' means, but nothing essential is missing for invoking it correctly.

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 coverage for the single event_id parameter is 100%, so the schema already documents the parameter. The description adds no further format, example, or source detail for event_id, leaving it at the baseline value.

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 uses a concrete verb ('Return') and identifies the resource ('recorded pipeline impact') plus the event-id key. It also defines the fallback result when no dashboard figure exists, making the tool's role unmistakable and distinct from the unrelated siblings.

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?

It clearly implies when to call: when pipeline-impact data for a given event_id is needed, and it explains what happens if the dashboard lacks the figure. It does not name alternative tools or explicitly say when not to use it, so it falls short of full 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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