Causal Decision Agent

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rlassologit

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Fit a high-dimensional logistic regression with rigorous Lasso or post-Lasso to select relevant predictors, enabling causal decision-making by isolating influential variables from large datasets.

Instructions

Logistic rigorous (post-)Lasso -- a faithful port of hdm::rlassologit.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`X`	Yes	Feature matrix or covariate DataFrame.
`y`	Yes	Outcome variable column name or outcome array.
`post`	No	If ``True``, refit the selected support by unpenalized logistic regression (post-Lasso); else keep the glmnet-penalized fit.
`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
`control`	No	``threshold`` -- coefficients below it are zeroed (default None).
`penalty`	No	Overrides for ``c`` (slack; default 1.1 for ``post=True``, else 0.5), ``gamma`` (default ``0.1/log n``) and ``lambda`` (raw penalty; bypasses the data-driven level).
`colnames`	No	Column names (default ``V1..Vp``).
`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://.
`intercept`	No	Include an intercept.
`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

C2.5/5.0

Behavior2/5

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

The annotations declare readOnlyHint=true and openWorldHint=false, providing a safety profile, but the description adds little beyond 'faithful port', which alerts the agent to R-package compatibility. It does not disclose how the model is fitted, what the result includes, or any edge-case behaviors.

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 definition is concise—one sentence with no filler—but it is genuinely under-specified rather than efficiently crafted. It conveys the provenance but omits essential context that an agent needs to invoke it correctly.

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?

Given 13 parameters, nested objects, and a large sibling set, the one-line description is incomplete. It does not explain when to use the tool, how the many options interact, or why one might choose it over rlassologit_effect or rlasso, relying entirely on the schema and annotations.

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%, with each of the 13 parameters having its own description. The tool-level description adds no parameter context, so the baseline of 3 applies because the schema already does the heavy lifting.

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

Purpose3/5

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

The description identifies the tool as a logistic rigorous (post-)Lasso port, which signals a statistical estimator but lacks an explicit verb phrase like 'fits' or 'estimates'. It does not distinguish it from closely related siblings such as rlassologit_effect, rlasso, or lasso_select, leaving the exact purpose somewhat ambiguous.

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?

There is no indication of when to use this tool versus alternatives, no mention of data prerequisites, and no examples. The description offers no guidance on selecting among the many sibling lasso/logit tools.

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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