Causal Decision Agent

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hausman_test

Read-only

Determines whether fixed effects or random effects model is appropriate for your panel data. Returns a test statistic and p-value to guide model selection.

Instructions

Hausman test for FE vs RE in panel data. Validation: validated evidence tier (known-truth, reference, external-parity, or Monte Carlo artifact).

Input Schema

TableJSON Schema

Name	Required	Description	Default
`x`	Yes	Independent variables (time-varying).
`y`	Yes	Dependent variable.
`id`	Yes	Unit identifier.
`time`	Yes	Time period identifier.
`alpha`	No	Significance level for confidence intervals and tests.
`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
`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`	Yes	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.
`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 readOnlyHint annotation already covers safety, and the description adds only a cryptic 'Validation: validated evidence tier ...' note rather than useful behavioral details like what the test consumes or produces. It does not contradict the annotations, but it provides limited behavioral transparency beyond them.

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 core purpose sentence is concise and front-loaded, but the second 'Validation: validated evidence tier ...' sentence is jargon-heavy and does not clearly help an agent select or invoke the tool. It reads like a metadata tag rather than guidance, so not every sentence 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?

Given the tool is a well-known statistical test with a complete schema and an output schema, the description covers the basic purpose. However, it omits any guidance on interpretation, prerequisites for the Hausman test, or relationship to sibling panel-data tools, and the validation sentence adds confusion rather than completeness.

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 the input schema fully documents all 11 parameters including required data_path, id, time, x, y, and optional alpha, detail, as_handle, etc. The description adds no parameter-specific meaning, so the baseline score of 3 is appropriate.

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 names the exact statistical procedure and target comparison ('Hausman test for FE vs RE in panel data'), which is clear and specific. It does not explicitly contrast with any sibling tool, but the FE-vs-RE scope distinguishes it from the many panel estimators and tests in the sibling list.

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 use case—choosing between fixed effects and random effects in panel data—but never states when to prefer this tool over alternatives or when not to use it. There are no explicit exclusions or comparisons to sibling tools such as other panel diagnostics, so the guidance is only implicit.

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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