Trillboards DOOH Advertising

get_incrementality

Get incrementality/lift test results for a campaign.

Uses Bayesian (Beta-Binomial with 10K Monte Carlo samples) and frequentist (chi-square with Yates correction) methods for causal measurement.

WHEN TO USE:

Proving causal DOOH advertising effectiveness
Getting both Bayesian and frequentist significance measures
Seeing treatment vs control group visit rates and lift

RETURNS: Array of experiments, each with:

experimentId, type (geo_holdout/ghost_ads/psm), status
treatmentDmas, controlDmas
latestResult: treatment/control rates, lift%, incrementalVisits, pValue, posteriorProbPositive, expectedUplift, credibleInterval

Returns empty array if no experiments exist for this campaign.

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": {
      "campaign_id": "<campaign_id>"
    },
    "name": "get_incrementality"
  }
}

Input Schema

Name	Required	Description	Default
`campaign_id`	Yes	Campaign identifier

Schema Changelog

Changes observed during successful MCP inspections.

First observedOct 20, 2026

TDQS

A4.3/5.0

Behavior4/5

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

No annotations are provided, so the description carries full weight. It discloses the statistical methods (Bayesian Beta-Binomial with 10K Monte Carlo samples, frequentist chi-square with Yates correction), the structure of the returned array (including latestResult fields), and the empty-array behavior. This goes well beyond a minimal description, though it does not mention side effects or authorization requirements.

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 well-structured with clear sections: a one-sentence summary, a brief methodology note, WHEN TO USE, and RETURNS. Every sentence adds value, with no padding or repetition.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Despite having no output schema, the description thoroughly covers return values: the array of experiments, types (geo_holdout/ghost_ads/psm), and nested latestResult fields (treatment/control rates, lift%, incrementalVisits, pValue, etc.). It also explains the empty-array case, making it complete for a read tool.

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 schema already provides 100% coverage for the single parameter campaign_id with a description. The tool description adds no additional parameter context, so the baseline of 3 applies.

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 states a specific verb+resource: 'Get incrementality/lift test results for a campaign.' This clearly distinguishes it from sibling tools like get_campaign_performance or get_roas, as it focuses on causal measurement and lift testing.

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?

A dedicated 'WHEN TO USE' section lists three explicit scenarios: proving causal DOOH effectiveness, obtaining Bayesian and frequentist significance measures, and seeing treatment vs. control group rates. However, it does not explicitly name alternatives or state when not to use, so it stops short of a 5.

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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