Causal Decision Agent

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survreg

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Fit parametric accelerated failure time survival models to time-to-event data, producing coefficients, diagnostics, and confidence intervals for evidence-backed rollout or hold decisions.

Instructions

Parametric survival model (AFT parameterization). Validation: certified parity evidence.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`x`	No	Covariate columns (or formula RHS).
`dist`	No	dist parameter (str).	weibull
`alpha`	No	Significance level for confidence intervals and tests.
`event`	No	Event indicator column.
`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 ``'duration ~ x1 + x2'``.
`duration`	No	Follow-up time column (or formula LHS).
`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

C2.6/5.0

Behavior2/5

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

The description mentions 'Validation: certified parity evidence' which hints at some reliability guarantee, but it doesn't disclose behavioral traits like side effects, permissions, or what happens to data. The readOnlyHint annotation covers the read-only nature, but the description adds little beyond that.

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 very concise (two sentences) but under-specified. The first sentence is informative, the second is vague and doesn't earn its place. It's not overly long, but it lacks the detail needed to be fully effective.

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 tool's complexity (14 parameters, output schema present), the description is extremely sparse. It doesn't explain the model's inputs, outputs, or typical usage patterns. The output schema may cover return values, but the description fails to convey essential usage context.

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 14 parameters are documented in the schema itself. The tool description doesn't add any parameter-specific meaning, but since the schema covers everything, the baseline of 3 applies.

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 parametric survival model with AFT parameterization, which distinguishes it from Cox and other survival approaches. It states a specific model type, so the purpose is clear, though it doesn't explicitly say 'fits' or 'estimates'.

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

Usage Guidelines1/5

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

There is no guidance on when to use this tool versus alternatives like Cox or other survival models. No conditions, prerequisites, or exclusions are mentioned, leaving the agent to infer usage context.

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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