Attribute local conversion events to tracked ad touchpoints using first-touch, last-touch, linear, or position-based models for accurate campaign analysis.
Attribute local conversion events to tracked touchpoints using a selected attribution model.
| Name | Required | Description | Default |
|---|---|---|---|
| to | No | ||
| from | No | ||
| model | Yes | ||
| project_id | No | ||
| campaign_id | No | ||
| lookback_days | No |
Changes observed during successful MCP inspections.
v0.1.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full behavioral burden, yet it discloses nothing about read-only status, computation cost, permissions, or output format. 'Attribute' hints at an analysis read, but an agent cannot confirm it is a safe non-mutating call from the text alone.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
A single front-loaded sentence with no filler; the core action leads. It is efficient, though the brevity arguably comes at the cost of the missing details noted elsewhere rather than being genuinely tight.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a 6-parameter tool with no annotations, no output schema, and zero schema description coverage, one sentence is far too thin. An agent lacks enough to call it correctly beyond supplying a model enum value.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage across 6 parameters, the description must compensate but only lightly touches the model parameter and vaguely gestures at from/to via 'conversion events' and 'touchpoints'. It says nothing about date format for from/to, the 0-90 range of lookback_days, or what project_id/campaign_id scope.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
States a specific verb (attribute) and resources (local conversion events, tracked touchpoints) plus the mechanism (selected attribution model). It reads as an analytics/report operation and is distinguishable from ads_event_ingest, though it does not explicitly contrast with the similar ads_network_conversion_report sibling.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No when-to-use guidance, no prerequisites, and no mention of alternatives such as ads_network_conversion_report or ads_campaign_analytics. The only implicit cue is that a model must be selected, which the schema already enforces as required.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.