Skip to main content
Glama
Sign In
Marketic

Marketic

by Das-rebel
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

get_attribution

Calculate multi-touch attribution across marketing channels using models like first-touch, last-touch, linear, time-decay, and position-based to assign conversion value to each touchpoint.

Instructions

Calculate multi-touch attribution across marketing channels using various models.

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": {
      "channel_points": "<channel_points>"
    },
    "name": "get_attribution"
  }
}

Input Schema

NameRequiredDescriptionDefault
modelNolinear
channel_pointsYesArray of {channel, touchpoints, conversion_value}

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?

With no annotations provided, the description carries the full burden of behavioral disclosure. It only states the tool 'calculates' attribution, without indicating whether it is read-only, what it returns, any side effects, or performance implications. The nature of the operation and its output are left unspecified, which is insufficient for an agent to understand its behavior safely.

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, efficient sentence that directly states the tool's purpose without redundancy. It is front-loaded with the primary action and resource, making it easily scannable. Every word serves a purpose, and there is no unnecessary detail.

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?

The tool lacks an output schema, and the description does not explain what the result looks like—whether it returns a detailed breakdown, a single metric, or a comparison of models. It also does not address error handling, data format expectations, or prerequisites. Given the moderate complexity and absent annotations, the description is clearly incomplete for guiding an agent through correct usage.

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?

The description mentions 'various models,' which adds some context to the model parameter, but it does not explain how to select among the five enum options. The channel_points parameter already has a schema description, and the description adds no further meaning there. With 50% schema coverage, the description partially compensates for the undocumented model parameter but remains limited in adding value 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 calculates multi-touch attribution across marketing channels using various models. It specifies a precise verb and resource, and its unique focus on attribution distinguishes it from sibling tools, none of which mention attribution in their names. An agent can confidently identify its purpose without confusion.

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 does not provide explicit guidance on when to use this tool instead of alternatives; it only states its function. The usage is implied by the purpose (attribution analysis), but there is no mention of exclusions, conditions, or reference to other tools. This meets the baseline for implied usage but does not offer clear direction on model selection or contextual recommendations.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Deploy Server

Other Tools