Causal Decision Agent

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biprobit

Read-only

Estimate a bivariate probit model to analyze two correlated binary outcomes simultaneously, enabling certified parity evidence for fair decision-making.

Instructions

Bivariate probit model. Validation: certified parity evidence.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`x1`	Yes	Regressors for equation 1.
`x2`	No	Regressors for equation 2. If None, same as x1.
`y1`	Yes	First binary outcome.
`y2`	Yes	Second binary outcome.
`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`	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

C2.2/5.0

Behavior2/5

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

With readOnlyHint=true in annotations, the safety profile is already known, so the description does not need to restate that. However, the description adds no useful behavioral context: it does not explain what 'certified parity evidence' means, what the fitting process returns, or any assumptions or side effects. There is no contradiction with annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness2/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is very short, but this is under-specification rather than effective conciseness. It contains no front-loaded action, no selection-relevant details, and the 'Validation' fragment is vague and does not 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 complex 15-parameter estimator with multiple optional features (x2, cluster, robust, detail, as_handle), the description is far too thin. The rich schema covers parameters, but the description leaves the tool's purpose, relationship to siblings, and validation semantics unexplained.

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 every parameter already documented in detail. The description itself contributes nothing beyond the tool name, so the baseline of 3 is appropriate: the schema carries the semantic burden.

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

Purpose2/5

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

The description only restates the tool name ('Bivariate probit model') without an explicit action verb like 'estimate,' 'fit,' or 'test.' It does not distinguish biprobit from the sibling 'probit' tool or other discrete-choice estimators, leaving the agent uncertain about the tool's exact role.

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 guidance on when biprobit should be chosen over alternatives such as probit, panel_probit, or ivreg. The cryptic phrase 'Validation: certified parity evidence' does not explain use conditions, prerequisites, or when not to use this tool.

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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