cox_frailty

Read-only

Fit Cox proportional hazards models with shared gamma frailty to analyze clustered survival data and estimate covariate effects while accounting for within-cluster dependence.

Instructions

Cox proportional hazards with shared gamma frailty. Validation: validated evidence tier (known-truth, reference, external-parity, or Monte Carlo artifact).

Input Schema

TableJSON Schema

Name	Required	Description	Default
`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
`cluster`	Yes	Column identifying clusters (e.g. hospital, site).
`formula`	Yes	``"duration + event ~ x1 + x2"`` (like R's ``Surv(time, event) ~ x``).
`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

B3.2/5.0

Behavior2/5

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

Annotations already declare readOnlyHint=trueched, and the description adds little beyond that. The validation-tier note mentions reliability but does not disclose what the tool returns, how the fit is performed, or any side effects. For a model-fitting tool with no mutation, the description does not carry additional behavioral context.

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

Conciseness4/5

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

The description is short and front-loaded with the essential purpose. The first sentence is crisp hole; but the second sentence about validation tier is tangential and may not help an agent decide to invoke the tool. It is not verbose, but the validation sentence adds little operational value.

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?

Given the tool's sophistication (Cox frailty) and a rich schema with 100% coverage, the description is mostly complete for parameter usage. However, it lacks context on when this model is chosen over simpler Cox or shared-frailty alternatives, and does not mention outputs or assumptions. Since an output schema exists, return values are covered, but the absence of usage context keeps this at a minimum viable level.

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 schema fully documents all 11 parameters. The description itself does not add any parameter-level meaning (e.g., the meaning of 'cluster' or 'formula' is left to the schema). Hence, a baseline score of 3 is appropriate.

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

Purpose5/5

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

The description clearly states the tool fits a Cox proportional hazards model with shared gamma frailty, immediately distinguishing it from plain Cox (cox) and other survival models like survival. It names the specific method and the resource it operates on.

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 about when to use this tool versus its siblings (e.g., cox, survreg, aft, logrank_test). It does not mention prerequisites, data requirements, or situations where a different survival tool would be appropriate. The only added sentence concerns validation tier, which is not a usage qualifier.

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