Causal Decision Agent

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ri_test

Read-only

Compute p-values via randomization inference by permuting treatment assignment, supporting difference-in-means, t, KS statistics, cluster permutations, and evidence validation.

Instructions

Randomization inference p-value. Validation: validated evidence tier (known-truth, reference, external-parity, or Monte Carlo artifact).

Input Schema

TableJSON Schema

Name	Required	Description	Default
`y`	Yes	Outcome variable.
`seed`	No	Random seed.
`stat`	No	Test statistic: - ``'diff_means'``: difference in means (Y_bar_1 - Y_bar_0) - ``'ks'``: Kolmogorov-Smirnov statistic - ``'t'``: t-statistic - A callable ``f(Y, D) -> float`` for custom statistics.	diff_means
`alpha`	No	Significance level for confidence intervals and tests.
`treat`	Yes	Binary treatment indicator (0/1).
`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
`cluster`	No	Cluster-level permutation (permute treatment at cluster level).
`n_perms`	No	Number of random permutations. Use 10000+ for publications.
`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

C2.9/5.0

Behavior3/5

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

The readOnlyHint annotation already establishes that this is a safe read-only operation, so the description does not need to cover side effects. It adds only a cryptic 'validated evidence tier' label and does not describe permutation behavior, assumptions, or what the p-value tests, but the annotations and output schema partially compensate.

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 sentence is telegraphic, partially tautological ('Validation: validated evidence tier'), and does not clearly earn its place.

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

Completeness2/5

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

For a 13-parameter statistical test in a large sibling family, the description omits when to use the tool, the null hypothesis, and the permutation mechanism. The schema and output schema cover parameters and return shape, but not tool-selection context.

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 baseline is 3 and the description need not repeat parameter documentation. The description itself adds no parameter-level meaning beyond the general concept of a p-value.

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 phrase 'Randomization inference p-value' states the estimator and the output clearly enough for an agent to identify this as a permutation-based hypothesis test. It lacks an explicit verb and does not explicitly differentiate from sibling testing tools, so it falls short of a 5.

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

Usage Guidelines2/5

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

No guidance is given on when to use ri_test versus sibling tools such as wild_cluster_bootstrap, fisher_exact, or anderson_rubin_test. The second sentence about 'validated evidence tier' reads as output metadata rather than usage direction.

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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