Causal Decision Agent

Overview Schema Source Related Servers Discussions Admin

oprobit

Read-only

Estimate ordered probit models via maximum likelihood to analyze ordered categorical outcomes, with certified parity evidence for validation.

Instructions

Ordered probit model via MLE. Validation: certified parity evidence.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`x`	No	Primary running variable, regressor, or feature input for this estimator.
`y`	No	Ordered categorical dependent variable.
`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.
`formula`	No	Formula ``"y ~ x1 + x2"``.
`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

C2.9/5.0

Behavior3/5

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

Annotations declare readOnlyHint=true, so the description is not required to state that the tool is read-only. The description adds the MLE estimation method and mentions validation, but does not disclose return format, side effects, or assumptions. It does not contradict annotations, but adds minimal behavioral context beyond the model type.

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 brief (two sentences) and front-loaded with the model type. However, the second sentence about 'certified parity evidence' is cryptic and unhelpful, detracting from the overall clarity. It is concise but the extra sentence 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?

Given the complexity (14 parameters, ordered probit), the description is insufficient. It does not explain the tool's purpose for ordered outcomes, clarify input requirements (e.g., what the 'x' array contains), or guide usage of formula or data_path. The output schema exists but the description does not help an agent decide when to use this over siblings.

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 all parameters are documented in the schema. The description itself adds no parameter semantics; it relies entirely on the schema. The baseline of 3 is appropriate given high schema coverage.

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 description clearly states 'Ordered probit model via MLE', which identifies the estimator type and estimation method. This distinguishes it from binary probit or ordered logit tools like 'probit' and 'ologit'. However, the phrase 'Validation: certified parity evidence' is ambiguous and does not add clarity, so it does not fully earn 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 this tool versus alternatives like 'ologit' or 'probit'. The description does not mention the need for an ordered categorical outcome, data requirements, or when not to use it. The agent must infer usage from the schema, which lacks explicit routing context.

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

Deploy Server