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Python

Hybrid

Recommend Campaign Budgets

recommend_campaign_budgets

Read-onlyIdempotent

Calculate campaign or adset budget recommendations from a completed MMM without applying changes, using read-only marginal returns and bounded allocation.

Instructions

Calculate campaign budget suggestions without applying them. Requires read:models.

Uses the same channel-derived marginal curves and bounded allocator as the web app. This POST is a read-only calculation: it creates no saved run, starts no fit or posterior job and does not change mappings or advertising-platform budgets. Results are conditional scenarios, not independently fitted campaign response curves or a day-by-day forecast.

Args: model_hash: Owned, completed MMM with verified curve and currency provenance. observation_window: {start: YYYY-MM-DD, end: YYYY-MM-DD}, inclusive fact dates. currency: Explicit currency matching the model and facts, for example GBP. channel_daily_budgets: Exact channel keys mapped to daily totals in that currency. Supply exactly one of this or optimizer_run_id. Preserve currency minor precision. optimizer_run_id: Existing owned optimiser run for the same model, with verified planning dates and currency. Its totals become flat daily equivalents, not a daily schedule. The tool does not create or rerun an optimiser. level: campaign or adset. max_step_fraction: Maximum change from observed average daily spend; default 0.2. bounds: Optional rows keyed by full identity {platform, account_id, campaign_id, adset_id}, with min and/or max amounts in daily currency units. Use null adset_id at campaign grain. No platform floor is invented; the server validates bounds. expected_context_key: Optional context_key returned by get_campaign_marginal_returns. A changed model, facts, mapping or attribution basis refuses with 409 instead of calculating against evidence different from the scenario you reviewed.

Returns {channels: [{channel, status, total_daily_budget, rows, explanation, assumptions, reason, feasible_range}], ...provenance}. Ready rows include current and recommended budgets, the continuous solution, marginal returns and binding constraints. Refusals retain their reasons and feasible ranges; never relax bounds or silently change a total. Rounded amounts reconcile in integer currency minor units; rounding does not imply exact equality of marginal returns. Missing marginal uncertainty remains explicitly unavailable.

Input Schema

TableJSON Schema

Name	Required	Default
`level`	No	campaign
`bounds`	No
`currency`	Yes
`model_hash`	Yes
`optimizer_run_id`	No
`max_step_fraction`	No
`observation_window`	Yes
`expected_context_key`	No
`channel_daily_budgets`	No

Output Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

Addedv0.16.0Oct 3, 2026

TDQS

A4.7/5.0

Behavior5/5

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

Goes well beyond the annotations: explains that this POST is a read-only calculation, creates no saved run, starts no fit/posterior job, and changes no mappings or platform budgets. It also discloses refusal semantics (409 on changed provenance), rounding reconciliation behavior, and that bounds are never silently relaxed.

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?

Front-loaded with purpose and permission, then a dense Args block that earns its length given 9 undocumented parameters. The Returns section is somewhat redundant since an output schema exists, though it adds refusal/rounding semantics.

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 9-parameter, nested-object, POST-based calculation tool with 0% schema coverage, the description covers auth, mutual exclusivity, defaults, provenance-refusal behavior, and output shape. Nothing critical to correct invocation is missing.

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 so thoroughly: format for observation_window, currency examples, the mutually exclusive channel_daily_budgets/optimizer_run_id, the default for max_step_fraction, and the exact key shape for bounds. Each parameter's meaning and constraints are clarified beyond the raw schema.

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 verb+resource ('Calculate campaign budget suggestions') and immediately scopes it ('without applying them'), which distinguishes it from run_optimizer and get_campaign_marginal_returns. The agent can tell what the tool produces without opening the schema.

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?

Gives strong usage conditions: 'Requires read:models', 'Supply exactly one of this or optimizer_run_id', and points to get_campaign_marginal_returns as the source for expected_context_key. It does not explicitly state when to prefer this over run_optimizer or show_optimizer_allocation, leaving that contrast 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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