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by getsimba-ai
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Python
Hybrid

Get Scenario Template

get_scenario_template
Read-onlyIdempotent

Generate a forward-period scenario template from a completed model, supplying channel names, baseline values, and average CPM to prepare for scenario runs or budget optimization.

Instructions

Generate a forward-period scenario template from a completed model.

Returns future dates pre-filled with values from 1 year prior, the list of media and control channels, and average cost-per-unit per media channel.

IMPORTANT: Always call this before run_scenario or run_optimizer to discover:

  • Channel names (use these exact names in scenario_data, bounds, laydown_weights, period_cpm)

  • Average CPM per channel (avg_cpu_by_channel — use for period_cpm in run_optimizer)

  • Baseline activity values per channel (rows — use as starting point for scenarios)

  • Media vs control channel classification (variable_classification field)

The response also includes: operating_margin (the model's stored margin, if set — useful for profit math), variable_transforms (per-variable transform metadata), periodicity, and start_date.

WARNING: Template data may contain NaN or null values for channels without historical data. You MUST replace NaN/null with 0 before passing to run_scenario, otherwise the prediction will fail downstream.

Args: model_hash: Hash of a completed model. periods_forward: Number of future periods to generate (default 12).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
model_hashYes
periods_forwardNo

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": "get_scenario_templateDictOutput",
+  "type": "object"
+}
  2. First observedv0.3.2

TDQS

A4.8/5.0
Behavior5/5

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

The description goes beyond the readOnlyHint and idempotentHint annotations by warning that template data may contain NaN/null values that must be replaced with 0 or the prediction will fail. It also discloses the presence of operating_margin, variable_transforms, and periodicity, adding behavioral context not available from annotations alone.

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?

The description is longer than average but well organized: a purpose sentence, response contents, an IMPORTANT block, a warning, and an Args section. There is minor redundancy between the initial response summary and the later IMPORTANT enumeration, but each section adds useful information and the critical usage directive is front-loaded.

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?

For a 2-parameter tool with an output schema, this description covers prerequisites (completed model hash), intended call order, return fields, NaN handling, and parameter defaults. Nothing critical for invoking the tool correctly is missing.

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

Parameters4/5

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

With schema description coverage at 0%, the description compensates by defining model_hash as 'Hash of a completed model' and periods_forward as the number of future periods to generate with a default of 12. This adds some meaning beyond the bare schema types, though the model_hash explanation remains somewhat terse.

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?

States a specific action — 'Generate a forward-period scenario template from a completed model' — and immediately differentiates itself from sibling execution tools by naming run_scenario and run_optimizer. The description also itemizes the template's contents, so an agent understands exactly what resource is produced.

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?

Provides an explicit directive: 'IMPORTANT: Always call this before run_scenario or run_optimizer to discover...' This clearly states when to use the tool. It also enumerates what information to extract (channel names, average CPM, baseline rows, classification) and warns about downstream failure, which is strong pragmatic guidance.

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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