Causal Decision Agent

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lrtest

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Compare a restricted and a full mixed model using a likelihood-ratio test to determine if the additional parameters significantly improve model fit.

Instructions

Likelihood-ratio test comparing a restricted and a full model. Validation: validated evidence tier (known-truth, reference, external-parity, or Monte Carlo artifact).

Input Schema

TableJSON Schema

Name	Required	Description	Default
`full`	Yes	Two fitted mixed models. ``full`` should strictly nest ``restricted`` -- i.e. the parameter space of the restricted model is a subset of the full model's.
`detail`	No	Payload depth: 'minimal' (~150 tokens) for sub-step calls where only the point estimate is needed; 'standard' (~1K tokens) for diagnostics + coefficient table; 'agent' (~2K tokens, default) adds violations / next_steps / suggested_functions so the LLM can plan its next call without another round-trip.	agent
`boundary`	No	Whether to apply the chi2 boundary correction. When ``None`` (default) we infer it from whether the restriction touches a variance component -- the only parameters that live on the boundary of their support.
`as_handle`	No	If true, cache the fitted result on the server and return result_id + result_uri alongside the JSON payload so a subsequent tools/call can chain without re-running.
`data_path`	No	Absolute path or URL to a data file. Supported: .csv / .tsv / .txt (delimited), .parquet / .pq, .feather / .arrow, .xlsx / .xls, .dta (Stata), .json / .jsonl. Schemes: file://, s3://, gs://, https://.
`result_id`	No	Optional handle to a previously-fitted result (returned by an earlier call when as_handle=true). Tools that operate on a fitted object accept this in place of re-supplying data_path + columns.
`restricted`	Yes	Two fitted mixed models. ``full`` should strictly nest ``restricted`` -- i.e. the parameter space of the restricted model is a subset of the full model's.
`data_columns`	No	Optional column projection. Parquet/Feather/Stata loaders honour this for fast partial reads.
`data_sample_n`	No	Optional uniform random subsample size (seed=0, deterministic) — useful on huge panels.

Output Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Sep 15, 2026

TDQS

B3.3/5.0

Behavior3/5

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

The annotations already declare readOnlyHint=true and openWorldHint=false, covering the safety profile. The description adds only the cryptic 'validated evidence tier' note, which hints at output validation but does not meaningfully explain behavior beyond what annotations provide.

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

Conciseness3/5

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

The description is short and front-loaded with the core purpose. However, the second 'Validation:' sentence is vague and does not clearly help an agent select or invoke the tool, so not every sentence fully earns its place.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The core purpose is clear and the schema/annotations cover much of the invocation contract. Still, for a tool with 9 parameters and a large sibling set, the description lacks explicit usage context, such as when to choose lrtest or what the validation tier actually means for the caller.

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

Parameters3/5

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

Schema description coverage is 100%, so all 9 parameters, including required full/restricted and the detail enum, are already documented in the schema. The tool description itself contributes no additional parameter semantics, matching the baseline for full schema coverage.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The first sentence clearly identifies the procedure as a 'likelihood-ratio test' comparing a 'restricted' and 'full' model. This is specific enough to distinguish it from generic model-testing siblings, though it does not explicitly name an alternative tool.

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

Usage Guidelines3/5

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

The description implies the intended use — compare a restricted model against a full model — and the schema reinforces strict nesting. However, it does not provide explicit guidance on when to prefer this tool over related test tools or state any exclusions.

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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