Causal Decision Agent

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truncreg

Read-only

Fit truncated regression models via maximum likelihood to analyze outcomes with lower or upper bounds, supporting robust and clustered standard errors for causal decision evidence.

Instructions

Truncated regression (MLE). Validation: certified parity evidence.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`x`	No	Regressors.
`y`	No	Outcome variable.
`ll`	No	Lower truncation point. None = no lower truncation.
`ul`	No	Upper truncation point. None = no upper truncation.
`tol`	No	Numerical convergence tolerance.
`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
`robust`	No	Robust standard-error or covariance estimator option.	nonrobust
`cluster`	No	Cluster identifier column for clustered standard errors.
`maxiter`	No	maxiter parameter (int).
`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.
`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

C2/5.0

Behavior1/5

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

Beyond the annotations (readOnlyHint=true), the description adds only the vague phrase 'Validation: certified parity evidence.' It does not disclose that the tool fits a model via MLE, how truncation bounds are handled, what output to expect, or any other behavioral detail that annotations do not already 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 very short and front-loaded with the method name, but the second sentence is opaque and adds little practical value. It is not padded, but the brevity sacrifices clarity and the cryptic 'validation' phrase does not earn its place.

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

Completeness1/5

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

Despite full schema coverage and an output schema, the description is severely incomplete. It does not explain what the tool does with the 15 parameters, when to choose truncated regression over similar models, or what the returned validation evidence refers to. An agent cannot reasonably decide to call this tool based on the description alone.

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 coverage is 100% and every parameter has a meaningful description, so the schema already carries the explanatory burden. The description itself adds no parameter-level information, making the baseline 3 appropriate.

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 'Truncated regression (MLE)' identifies the statistical method but lacks an explicit verb like 'fit' or 'estimate'. It does not differentiate from similar siblings like tobit, and the 'Validation: certified parity evidence' phrase is cryptic and does not clarify the tool's core purpose.

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

Usage Guidelines1/5

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

There is no guidance on when to use truncated regression versus alternatives such as tobit or censored regression. No mention of data requirements, truncation point semantics, or typical use cases, so an agent has no basis for selecting this tool over its many siblings.

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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