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Import Incrementality Tests

import_incrementality_tests

Load incrementality test results into Simba from CSV, Conversion Lift, GeoX, GeoLift, CausalPy, or PyMC Marketing outputs, with dry-run validation before creating records.

Instructions

Import tests from another tool's output file: source is csv (Simba's template), meta_conversion_lift (Conversion Lift API results JSON), geox (a meridian-geox analysis result), geolift (GeoLift summary), causalpy (effect summary or lift rows) or pymc_marketing (lift rows); content is the file's text (10 MB max). Returns {records: [{key, record, errors}], created, notes}. dry_run (default true) creates nothing — review each row's errors, then call again with dry_run=false to create the rows without errors (their ids come back in created). defaults fills fields the file doesn't carry, e.g. {"channel": "TV", "model_channel": "tv_grps", "kpi": {"kind": "revenue"}}: a GeoX result names no channel or KPI, so its rows fail until those are given. overrides sets fields on one row by the key the dry run showed (a cell id, or the 1-based row number), e.g. {"1": {"spend": {"incremental": 25000}}}. Values deep-merge: the source's assumptions, then defaults, then the file, then overrides; a null removes a field. import_invalid means the file isn't that source's format. Requires the create:models scope.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`source`	Yes
`content`	Yes
`dry_run`	No
`defaults`	No
`overrides`	No
`project_id`	Yes

Output Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

Addedv0.12.0Sep 30, 2026

TDQS

A4.9/5.0

Behavior5/5

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

Beyond the annotations, the description discloses the dry-run safety workflow, the create:models scope requirement, the return shape, and the precise deep-merge precedence (source assumptions, then defaults, then file, then overrides) including that a null removes a field. That is rich behavioral context an agent cannot get from the structured fields.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

It is correctly front-loaded and every clause carries information, but it is delivered as one dense semicolon-chained paragraph, which slightly hurts scannability for a six-parameter tool.

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?

Given an output schema exists, return values need not be explained, yet the description still summarizes them; combined with the scope, merge-order, and per-source failure notes, an agent has everything needed to call this correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description carries the full burden and does: it defines each source enum value, the 10 MB content limit, dry_run's default, and gives concrete examples for defaults and overrides including key formats (cell id or 1-based row number). This is more than the schema conveys.

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 opens with a specific verb+resource ('Import tests from another tool's output file') and enumerates each supported source format, which clearly distinguishes it from siblings like create_incrementality_test and list_incrementality_tests.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

It gives explicit workflow guidance: dry_run defaults to true and creates nothing, so the agent should review each row's errors then re-call with dry_run=false. It also names the failure condition (import_invalid) and the scope required (create:models), leaving little to inference.

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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