meologit

Read-only

Fit random-effects ordinal logistic models to ordered outcomes with grouped data, producing coefficients, diagnostics, and actionable next-step suggestions for causal analysis.

Instructions

Random-effects ordinal logit (Stata meologit, R ordinal::clmm).

Input Schema

TableJSON Schema

Name	Required	Description	Default
`y`	Yes	Outcome variable column name or outcome array.
`tol`	No	Numerical convergence tolerance.
`nAGQ`	No	nAGQ parameter (int).
`alpha`	No	Significance level for confidence intervals and tests.
`group`	Yes	Group or cohort identifier.
`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
`offset`	No	Offset term or offset column.
`maxiter`	No	maxiter parameter (int).
`x_fixed`	Yes	x_fixed parameter (Sequence[str]).
`cov_type`	No	Covariance estimator type.	unstructured
`x_random`	No	x_random parameter (Optional[Sequence[str]]).
`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

A3.5/5.0

Behavior3/5

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

The readOnlyHint annotation already covers the safety profile, so the bar for additional behavioral disclosure is lower. The description adds the Stata and R implementation equivalents, which provide some context about the estimator, but it does not describe convergence behavior, caching via as_handle, or what happens with large panels.

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 tight sentence with no filler or redundancy. It front-loads the model name and immediately gives cross-software references, which is efficient for an agent scanning many sibling tools.

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 schema is rich and covers parameters, and an output schema exists, so the description does not need to explain return values. However, given the very large sibling set and the absence of explicit usage guidance, the one-line description is minimal but not fully complete for a 16-parameter model-fitting tool.

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 the baseline is 3 and the parameters are already well-documented in the input schema. The description itself does not explain any parameters, and while the Stata/R references hint at syntax conventions, they add little semantic value beyond the schema.

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 identifies the exact statistical method: 'Random-effects ordinal logit,' which is specific and distinguishes it from siblings like melogit (random-effects logit) and ologit (ordinal logit without random effects). It lacks an explicit verb such as 'fits' or 'estimates,' but the model-family name makes the operation clear.

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

Usage Guidelines3/5

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

The model type implies usage: ordinal outcomes with random effects, so an agent can infer when to select it over melogit or ologit. However, the description never explicitly states when to use this tool versus alternatives, nor does it mention data assumptions or exclusions such as binary outcomes.

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

Deploy Server

Causal Decision Agent