Causal Decision Agent

Overview Schema Source Related Servers Discussions Admin

wild_cluster_boot

Read-only

Run a wild cluster bootstrap t-test to evaluate a single coefficient's significance, delivering cluster-robust p-values and confidence intervals.

Instructions

Wild cluster bootstrap t-test for a single coefficient.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`seed`	No	Random seed for reproducibility.
`alpha`	No	Significance level for confidence interval.
`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
`n_boot`	No	Number of bootstrap replications (use odd number).
`result`	Yes	A fitted regression result from ``sp.regress()``.
`cluster`	Yes	Name of the cluster variable.
`variable`	Yes	Name of the coefficient to test (H0: beta = 0).
`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.
`weight_type`	No	Bootstrap weight distribution: - ``'rademacher'``: +/-1 with equal probability. - ``'webb'``: Webb (2014) 6-point distribution for G < 12. - ``'mammen'``: Mammen (1993) 2-point distribution.	rademacher
`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

A3.5/5.0

Behavior3/5

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

The annotations declare readOnlyHint=true and openWorldHint=false, which the description does not contradict. However, the description adds no additional behavioral context beyond what the annotations provide—it does not describe return values, side effects, or any caveats about cluster count or weight distribution. With annotations covering the safety profile, the description adds minimal value on this dimension.

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 a single, front-loaded sentence that captures the core purpose without any fluff. It is concise and immediately informative.

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 tool has 13 parameters and an output schema, which carry much of the load. However, the description lacks any context about when to use this variant over closely related tools (wild_cluster_bootstrap, subcluster_wild_bootstrap), and it doesn't explain the statistical purpose beyond the name. While the schema and output schema fill many gaps, the absence of usage context makes it only minimally complete for an agent deciding between similar tools.

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 every parameter already has a description. The tool description itself adds no parameter-specific information beyond what's in the schema, so it remains at the baseline 3.

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 clearly identifies the operation: a wild cluster bootstrap t-test applied to a single coefficient. It distinguishes this tool from siblings like wild_cluster_bootstrap and subcluster_wild_bootstrap by the explicit 'single coefficient' scope, so an agent can select it appropriately.

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 provided on when to use this tool versus closely related siblings such as wild_cluster_bootstrap, subcluster_wild_bootstrap, or wild_cluster_ci_inv. There is no mention of prerequisites, such as needing a fitted regression result, nor any conditions that would favor this tool over alternatives.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Deploy Server