Causal Decision Agent

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kaplan_meier

Read-only

Estimate survival probabilities from time-to-event data, handle censored observations, and compare survival across groups with confidence intervals.

Instructions

Kaplan-Meier non-parametric survival function estimator. Validation: certified parity evidence.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`alpha`	No	Significance level for confidence intervals (Greenwood formula).
`event`	Yes	Column name for event indicator (1 = event, 0 = censored).
`group`	No	Column name for group variable (stratification).
`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
`duration`	Yes	Column name for duration / follow-up time.
`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

B3.1/5.0

Behavior3/5

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

The readOnlyHint annotation already covers non-mutation, and the description adds that the estimator is non-parametric, which is useful behavioral context. However, the second sentence 'Validation: certified parity evidence' is vague and provides no meaningful behavioral disclosure about censoring, output structure, or limitations.

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 primary sentence is concise and front-loaded, but the 'Validation: certified parity evidence' sentence does not meaningfully help an agent select or invoke the tool. The description could be tighter and more informative.

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?

With a complete input schema, an output schema, and readOnly annotations, much of the operational context is covered. Still, the description omits when Kaplan-Meier should be preferred over regression-based survival tools and does not clarify the role of the group parameter or hypothesis testing.

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 ten parameters are already documented in the schema. The tool description itself adds no parameter-level meaning beyond what the schema provides, leaving the baseline of 3 appropriate.

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 identifies the tool as a Kaplan-Meier non-parametric survival function estimator. The verb is implied rather than explicit, and it does not differentiate from survival siblings such as cox, survreg, or logrank_test, but the core purpose is recognizable.

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 explicit guidance is given about when to use this tool versus alternatives like cox, survreg, or survival_sensitivity. The description does not mention that Kaplan-Meier is appropriate for unadjusted survival estimation or that it does not handle covariates.

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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