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link_var_model

Link a VAR model to an MMM to merge long-run elasticities with short-term revenue. Optionally map VAR series to MMM channels for accurate allocation.

Instructions

Link a completed VAR model to an MMM (#569).

After linking, the MMM's get_model_results long_run_rollup section joins the VAR's long-run elasticities with the MMM's short-term revenue. A VAR links to at most one MMM at a time — the error names the current owner if it is already linked elsewhere.

The join is by exact name unless channel_map declares which MMM channels each VAR exogenous series stands for (#682) — required whenever the VAR is fitted on group spends (e.g. four spend groups) while the MMM is tactic-level. Each group's elasticity is allocated across its member channels pro-rata by KPI short-term contribution, so the group's long-run effect is counted exactly once. Validation is strict: keys must be VAR exogenous series, values must be channel names of the (completed) MMM, and no channel may belong to two groups. The map belongs to the link: every link replaces it (omitting channel_map clears any stored map) and unlink clears it.

Args: model_hash: The MMM to attach the long-run view to. var_model_hash: The VAR model (from create_var_model). channel_map: Optional {var_exogenous_series: [mmm_channel, ...]} mapping for group-level VARs.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
model_hashYes
channel_mapNo
var_model_hashYes
Behavior5/5

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

With no annotations, the description carries the full behavioral burden and does so thoroughly: it discloses side effects (map replacement, unlink clearing), the pro-rata allocation rule, exact-name matching, strict validation rules, and the error naming the current owner for an already-linked VAR.

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 detailed but front-loaded: one-sentence purpose, then behavior, then validation, then args. The length is justified by the tool's complexity (group allocation, map lifecycle, strict validation) and every paragraph adds operational information rather than repeating schema.

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 three parameters, no annotations, and no output schema, the description provides enough for a correct call: required vs optional args, channel_map semantics, validation, ownership constraint, and resulting behavior in get_model_results. No critical operational gap is apparent.

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 Args section must compensate. It defines model_hash as the MMM to attach, var_model_hash as the VAR from create_var_model, and explains channel_map's shape, optionality, validation, and link-scoped lifecycle. This goes well beyond the bare 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?

The description opens with a specific verb and resource: 'Link a completed VAR model to an MMM.' It clarifies the linking relationship and its observable effect on get_model_results, and the restrictions distinguish it from unlink_var_model and other model tools.

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?

It explains why linking is useful (long_run_rollup joins VAR elasticities with MMM revenue), when channel_map is required ('required whenever the VAR is fitted on group spends ... while the MMM is tactic-level'), and notes the one-link-per-VAR constraint. It does not explicitly say 'use unlink_var_model to remove a link,' though the unlink behavior is mentioned.

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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