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Simba MCP Server

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by getsimba-ai
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Python
Hybrid

Set Contribution Groups

set_contribution_groups
DestructiveIdempotent

Configure contribution driver groupings once and persist them so every dashboard viewer sees the same setup. Supports colors and base adjustments with validation against model factors.

Instructions

Persist the driver groupings the dashboard contributions view renders (#436) — configure grouping once and every viewer sees it.

Each group: {"name": str, "drivers": [column names], "color": "#hex"?, "baseAdjustments": {driver: "min"|"max"|"none"}?}. Driver names are validated against the model's media/control/halo/trademark factors (400 with a did-you-mean hint on typos); each driver may belong to at most one group; baseAdjustments must reference the group's own drivers. The special "_channel_color_overrides" pseudo-group carries a channelColors map instead of drivers.

NOTE: this is the CONTRIBUTIONS-VIEW grouping. create_model's channel_groups is the unrelated adstock parameter-sharing feature — do not confuse them.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
model_hashYes
contribution_groupsYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changedv0.5.0
    • changedOutput schema / (root)
      Previous value: -nullNew value: +{
+  "additionalProperties": true,
+  "title": "set_contribution_groupsDictOutput",
+  "type": "object"
+}
  2. First observedv0.3.2

TDQS

A5/5.0
Behavior5/5

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

Beyond the annotations (readOnlyHint=false, idempotentHint=true, destructiveHint=true), the description adds rich behavioral context: validation against model factors, 400 responses with did-you-mean hints, one-group-per-driver constraints, baseAdjustments scoping, and the special _channel_color_overrides pseudo-group. No contradiction with annotations.

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 compact despite covering complex validation rules. It front-loads the core purpose, then gives the group shape, constraints, and a disambiguation note—each sentence adds necessary information without 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?

Given the tool's complexity, the description is complete: it explains the object format, validation behavior, persistence semantics, and the unrelated sibling feature. An output schema exists, so return-value documentation is not required from the description.

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. It supplies the exact structure of each contribution group, including optional fields, accepted value types, and validation semantics. This is far more than the input schema provides.

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 pair: 'Persist the driver groupings the dashboard contributions view renders.' It also explicitly contrasts this tool with create_model's channel_groups, making the purpose unmistakable and distinguishing it from a plausible sibling.

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?

The description clearly states this is the CONTRIBUTIONS-VIEW grouping and explicitly warns not to confuse it with create_model's channel_groups, naming the alternative feature as unrelated. This gives the agent a concrete when-not-to-use signal and prevents cross-tool confusion.

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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