Causal Decision Agent

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subgroup_analysis

Read-only

Run subgroup heterogeneity analysis to see how an explanatory variable’s effect varies across groups, then display the results in a forest plot for quick comparison.

Instructions

Run subgroup heterogeneity analysis with forest plot.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`x`	Yes	Key explanatory variable.
`by`	Yes	Mapping of display name -> column name for grouping.
`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	Standard error type for subgroup regressions.	hc1
`formula`	Yes	Regression formula, e.g. ``"wage ~ education + experience"``.
`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 declares the tool read-only; the description's 'Run... analysis' is consistent. It adds the forest plot output detail, which is useful context, but does not describe further behaviors such as output structure or required input formats.

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 a single, front-loaded sentence with no filler. It is concise but conveys only minimal information, which is appropriate for a simple tool but not exceptional.

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 large sibling list and the tool's specific output (forest plot), the description is under-specified: it does not explain when to use subgroup analysis, how 'subgroup' relates to the 'by' parameter, or how it differs from subgroup_decompose. The schema and annotations are thorough, but the description itself leaves too much to inference.

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 already well documented. The description adds no parameter-level meaning beyond the schema, warranting the baseline score of 3.

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 states a specific verb ('Run') and resource ('subgroup heterogeneity analysis'), and mentions the forest plot output, which helps distinguish it from related analysis tools. It does not explicitly name a sibling tool, but the action and output are clear enough.

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?

There is no guidance on when to use this tool versus sibling alternatives like subgroup_decompose or cate_by_group. The description gives no prerequisites, conditions, or exclusions, leaving the agent to infer suitability from the name alone.

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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